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huyue012/wav2vec2-base-cynthia-tedlium

huyue012

transformers

automatic-speech-recognition

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-cynthia-tedlium results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-cynthia-tedlium This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.15.1 - Tokenizers 0.10.3

toanbku/oa-falcon-7b-sft-df

toanbku

transformers

text-generation

--- language: - en tags: - open-assitant - falcon license: "unknown" datasets: - toanbku/oa-df --- - Datasets: https://huggingface.co/datasets/toanbku/oa-df - Training log: https://wandb.ai/toanbku/supervised-finetuning/runs/w1l8j7n6/overview Command ``` export BS=8 deepspeed --include=localhost:0,1,2,3,4,5,6,7 --master_port 61000 trainer_sft.py \ --config defaults oa-falcon-7b-top1 oasst_df \ --cache_dir /home/ubuntu/OA/model/model_training/.cache \ --per_device_eval_batch_size $BS --per_device_train_batch_size $BS \ --deepspeed ``` Config ``` oa-falcon-7b-top1: dtype: bf16 log_dir: "falcon_log_7b" learning_rate: 1e-5 model_name: "OpenAssistant/falcon-7b-sft-top1-696" deepspeed_config: configs/zero_config.json output_dir: falcon weight_decay: 0.0 max_length: 2048 save_strategy: steps eval_steps: 80 save_steps: 80 warmup_steps: 4 gradient_checkpointing: true gradient_accumulation_steps: 2 per_device_train_batch_size: 2 per_device_eval_batch_size: 4 num_train_epochs: 4 save_total_limit: 2 residual_dropout: 0.2 residual_dropout_lima: true oasst_df: save_strategy: epoch datasets: - oasst_export: lang: "en" hf_dataset_name: toanbku/oa-df val_split: 0.05 ``` ### Demo - **input_text:** <|prompter|>Provide information about Dwarves Foundation company<|endoftext|><|assistant|> - **output:** ``` Dwarves Foundation is a blockchain-based, decentralized platform that aims to create a global community based on equality and inclusion. It was founded by Alex Mashinsky, an entrepreneur and investor. The platform uses blockchain technology to create a decentralized and transparent system for the creation and management of projects. Dwarves Foundation allows users to create and participate in projects that promote equality and inclusion. Users can contribute time, money, or other resources to projects, and they are rewarded with DVA, the platform's native cryptocurrency. This cryptocurrency can be used to participate in future projects or to purchase goods and services within the Dwarves Foundation ecosystem. The platform aims to create a community that is diverse and inclusive, and it encourages people from all walks of life to participate. It believes that collaboration and teamwork are key to solving the world's problems, and it aims to create a platform where people can work together to make the world a better place. So far, Dwarves Foundation has been successful in creating a community and launching several projects. One of its most successful projects, the AION network, is a decentralized platform that allows users to create and participate in projects using the DVA cryptocurrency. The platform has already attracted thousands of users and has several projects in development. Overall, Dwarves Foundation is a unique platform that aims to create a global community based on equality and inclusion. It uses blockchain technology to create a transparent and decentralized system, and it encourages people from all walks of life to participate and collaborate towards a better future. ``` - **log:** ``` python ./test.py Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision. Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision. Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision. [2023-07-17 11:32:36,837] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.98s/it] The model 'RWForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM']. /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/transformers/generation/utils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation) warnings.warn( Setting `pad_token_id` to `eos_token_id`:11 for open-end generation. ``` ---- ### Training log ``` (cuda118) [RedmondAI] ubuntu@oa-server-8:~/OA/model/model_training$ deepspeed --include=localhost:0,1,2,3,4,5,6,7 --master_port 61000 trainer_sft.py --config defaults oa-falcon-7b-top1 oasst_df --cache_dir /home/ubuntu/OA/model/model_training/.cache --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --deepspeed [2023-07-17 16:21:13,138] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:16,536] [WARNING] [runner.py:196:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. [2023-07-17 16:21:16,536] [INFO] [runner.py:555:main] cmd = /home/ubuntu/mambaforge/envs/cuda118/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=61000 --enable_each_rank_log=None trainer_sft.py --config defaults oa-falcon-7b-top1 oasst_df --cache_dir /home/ubuntu/OA/model/model_training/.cache --per_device_eval_batch_size 8 --per_device_train_batch_size 8 --deepspeed [2023-07-17 16:21:17,929] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:20,292] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} [2023-07-17 16:21:20,292] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 [2023-07-17 16:21:20,292] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) [2023-07-17 16:21:20,292] [INFO] [launch.py:163:main] dist_world_size=8 [2023-07-17 16:21:20,292] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 [2023-07-17 16:21:24,714] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:24,805] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:25,000] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:25,151] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:25,228] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:25,251] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:25,295] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-17 16:21:25,299] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) trainig_conf = Namespace(rng_seed=2703368087, learning_rate='1e-5', gradient_checkpointing=True, gradient_accumulation_steps=2, per_device_train_batch_size=8, per_device_eval_batch_size=8, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon='1e-12', weight_decay=0.0, warmup_steps=4, eval_steps=80, save_strategy='epoch', save_steps=80, max_length=2048, val_max_length=None, num_train_epochs=4, logging_steps=10, max_grad_norm=2.0, save_total_limit=2, dtype='bf16', eval_accumulation_steps=None, freeze_layer=None, datasets=[{'oasst_export': {'lang': 'en', 'hf_dataset_name': 'toanbku/oa-df', 'val_split': 0.05}}], datasets_extra=[], cache_dir='/home/ubuntu/OA/model/model_training/.cache', loss_fn='CrossEntropyLoss', eval_size=None, log_dir='falcon_log_7b', quantization=False, seq2seqmodel=False, poly_eps=1.0, fuse_gelu=True, log_wandb=True, samples_mixing=False, verbose=False, output_dir='falcon', use_custom_sampler=False, random_offset_probability=0.8, label_masking=True, residual_dropout=0.2, use_flash_attention=False, sort_by_length=False, use_system_prefix=False, system_prefix='You are Joi, a large language model trained by Open-Assistant. Answer as concisely as possible.\nKnowledge cutoff: 2021-09-01\nCurrent date: 2023-03-12', use_system_tag=False, system_property_dropout=0.5, system_add_length=False, per_digit_tokens=False, is_reward_model=False, residual_dropout_lima=True, deepspeed_config='configs/zero_config.json', peft_model=False, peft_type='lora', model_name='OpenAssistant/falcon-7b-sft-top1-696', wandb_entity='toanbku', local_rank=0, deepspeed=True, resume_from_checkpoint=False, show_dataset_stats=False, world_size=8) [2023-07-17 16:21:25,864] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:25,864] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:25,864] [INFO] [comm.py:625:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl [2023-07-17 16:21:25,952] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:25,952] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:26,311] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:26,312] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:26,320] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:26,320] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:26,407] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:26,407] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:26,511] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:26,512] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:26,558] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:26,558] [INFO] [comm.py:594:init_distributed] cdb=None [2023-07-17 16:21:26,618] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2023-07-17 16:21:26,619] [INFO] [comm.py:594:init_distributed] cdb=None RNG seed: 2703368087 RNG seed: 2703368087 RNG seed: 2703368087 RNG seed: 2703368087 RNG seed: 2703368087 RNG seed: 2703368087 RNG seed: 2703368087 RNG seed: 2703368087 Tokenizer sanity check: Type: PreTrainedTokenizerFast special_tokens_map: {'eos_token': '<|endoftext|>', 'sep_token': '<|endoftext|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|prompter|>', '>>SUFFIX<<', '<|prefix_begin|>', '>>INTRODUCTION<<', '>>QUESTION<<', '>>SUMMARY<<', '<|prefix_end|>', '>>DOMAIN<<', '<|assistant|>', '<|system|>', '>>TITLE<<', '>>COMMENT<<', '>>MIDDLE<<', '>>PREFIX<<', '>>ANSWER<<', '>>ABSTRACT<<']} Using bos_token, but it is not set yet. bos_token='None', bos_token_id=None eos_token='<|endoftext|>', eos_token_id=11 prompter_token_id=65028, assistant_token_id=65025 encoding result: {'input_ids': [65028, 60, 28, 11, 65024, 13318, 37, 445, 193, 7055, 37, 204, 28, 193, 11723, 37, 20906, 193, 11, 65025, 44, 28, 11, 65028, 60, 29, 11, 65024, 7055, 37, 204, 28, 193, 13318, 37, 445, 193, 11723, 37, 20906, 193, 11, 65025, 44, 29, 11], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} 0: 65028 -> "<|prompter|>" 1: 60 -> "Q" 2: 28 -> "1" 3: 11 -> "<|endoftext|>" 4: 65024 -> "<|system|>" 5: 13318 -> "lang" 6: 37 -> ":" 7: 445 -> " en" 8: 193 -> " " 9: 7055 -> "length" 10: 37 -> ":" 11: 204 -> " " 12: 28 -> "1" 13: 193 -> " " 14: 11723 -> "context" 15: 37 -> ":" 16: 20906 -> " ctx" 17: 193 -> " " 18: 11 -> "<|endoftext|>" 19: 65025 -> "<|assistant|>" 20: 44 -> "A" 21: 28 -> "1" 22: 11 -> "<|endoftext|>" 23: 65028 -> "<|prompter|>" 24: 60 -> "Q" 25: 29 -> "2" 26: 11 -> "<|endoftext|>" 27: 65024 -> "<|system|>" 28: 7055 -> "length" 29: 37 -> ":" 30: 204 -> " " 31: 28 -> "1" 32: 193 -> " " 33: 13318 -> "lang" 34: 37 -> ":" 35: 445 -> " en" 36: 193 -> " " 37: 11723 -> "context" 38: 37 -> ":" 39: 20906 -> " ctx" 40: 193 -> " " 41: 11 -> "<|endoftext|>" 42: 65025 -> "<|assistant|>" 43: 44 -> "A" 44: 29 -> "2" 45: 11 -> "<|endoftext|>" message_indices: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3] Downloading and preparing dataset json/toanbku--oa-df to /home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50.4k/50.4k [00:00<00:00, 45.8MB/s] Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.5k/11.5k [00:00<00:00, 38.3MB/s] Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 11.19it/s] Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1782.53it/s] Dataset json downloaded and prepared to /home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96. Subsequent calls will reuse this data. Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/toanbku___json/toanbku--oa-df-811abf2c8473a2c5/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96) /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/utils/data/dataset.py:348: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset. warnings.warn(f"Length of split at index {i} is 0. " OASST HF dataset toanbku/oa-df: len(train)=32, len(val)=0 Downloading builder script: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.20k/4.20k [00:00<00:00, 11.1MB/s] Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision. Downloading builder script: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.20k/4.20k [00:00<00:00, 10.9MB/s] Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision. 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Use `wandb login --relogin` to force relogin wandb: Tracking run with wandb version 0.15.5 wandb: Run data is saved locally in /home/ubuntu/OA/model/model_training/wandb/run-20230717_162731-w1l8j7n6 wandb: Run `wandb offline` to turn off syncing. wandb: Syncing run OpenAssistant/falcon-7b-sft-top1-696-falcon_log_7b-finetuned wandb: ⭐️ View project at https://wandb.ai/toanbku/supervised-finetuning wandb: 🚀 View run at https://wandb.ai/toanbku/supervised-finetuning/runs/w1l8j7n6 Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Creating extension directory /home/ubuntu/.cache/torch_extensions/py310_cu118/fused_adam... Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Detected CUDA files, patching ldflags Emitting ninja build file /home/ubuntu/.cache/torch_extensions/py310_cu118/fused_adam/build.ninja... Building extension module fused_adam... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... Using /home/ubuntu/.cache/torch_extensions/py310_cu118 as PyTorch extensions root... [1/3] /home/ubuntu/mambaforge/envs/cuda118/bin/nvcc -ccbin /home/ubuntu/mambaforge/envs/cuda118/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/deepspeed/ops/csrc/includes -I/home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/deepspeed/ops/csrc/adam -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include/TH -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include/THC -isystem /home/ubuntu/mambaforge/envs/cuda118/include -isystem /home/ubuntu/mambaforge/envs/cuda118/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -lineinfo --use_fast_math -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_80,code=compute_80 -DBF16_AVAILABLE -std=c++17 -c /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/deepspeed/ops/csrc/adam/multi_tensor_adam.cu -o multi_tensor_adam.cuda.o [2/3] /home/ubuntu/mambaforge/envs/cuda118/bin/x86_64-conda-linux-gnu-c++ -MMD -MF fused_adam_frontend.o.d -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/deepspeed/ops/csrc/includes -I/home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/deepspeed/ops/csrc/adam -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include/TH -isystem /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/include/THC -isystem /home/ubuntu/mambaforge/envs/cuda118/include -isystem /home/ubuntu/mambaforge/envs/cuda118/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++17 -O3 -std=c++17 -g -Wno-reorder -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -DBF16_AVAILABLE -c /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/deepspeed/ops/csrc/adam/fused_adam_frontend.cpp -o fused_adam_frontend.o [3/3] /home/ubuntu/mambaforge/envs/cuda118/bin/x86_64-conda-linux-gnu-c++ fused_adam_frontend.o multi_tensor_adam.cuda.o -shared -L/home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/home/ubuntu/mambaforge/envs/cuda118/lib64 -lcudart -o fused_adam.so Loading extension module fused_adam... Time to load fused_adam op: 29.23199462890625 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.244072675704956 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.24746584892273 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.145082473754883 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.245840072631836 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.245012760162354 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.24890160560608 seconds Loading extension module fused_adam... Time to load fused_adam op: 29.146907329559326 seconds Rank: 6 partition count [8] and sizes[(865224176, False)] Rank: 4 partition count [8] and sizes[(865224176, False)] Rank: 3 partition count [8] and sizes[(865224176, False)] Rank: 7 partition count [8] and sizes[(865224176, False)] Rank: 5 partition count [8] and sizes[(865224176, False)] Rank: 0 partition count [8] and sizes[(865224176, False)] Rank: 2 partition count [8] and sizes[(865224176, False)] Rank: 1 partition count [8] and sizes[(865224176, False)] You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. 0%| | 0/4 [00:00<?, ?it/s]You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. 25%|███████████████████████████████████████████████▊ | 1/4 [00:00<00:02, 1.05it/s/home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( /home/ubuntu/mambaforge/envs/cuda118/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details. warnings.warn( {'train_runtime': 603.8881, 'train_samples_per_second': 0.212, 'train_steps_per_second': 0.007, 'train_loss': 1.333984375, 'epoch': 4.0} 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [10:03<00:00, 150.97s/it] [2023-07-17 16:38:44,427] [INFO] [launch.py:347:main] Process 46522 exits successfully. [2023-07-17 16:38:44,428] [INFO] [launch.py:347:main] Process 46521 exits successfully. [2023-07-17 16:38:44,428] [INFO] [launch.py:347:main] Process 46520 exits successfully. [2023-07-17 16:38:44,429] [INFO] [launch.py:347:main] Process 46518 exits successfully. [2023-07-17 16:38:44,429] [INFO] [launch.py:347:main] Process 46523 exits successfully. [2023-07-17 16:38:45,431] [INFO] [launch.py:347:main] Process 46524 exits successfully. [2023-07-17 16:38:45,432] [INFO] [launch.py:347:main] Process 46519 exits successfully. wandb: Waiting for W&B process to finish... (success). wandb: wandb: Run history: wandb: train/epoch ▁ wandb: train/global_step ▁ wandb: train/total_flos ▁ wandb: train/train_loss ▁ wandb: train/train_runtime ▁ wandb: train/train_samples_per_second ▁ wandb: train/train_steps_per_second ▁ wandb: wandb: Run summary: wandb: train/epoch 4.0 wandb: train/global_step 4 wandb: train/total_flos 1903271472005120.0 wandb: train/train_loss 1.33398 wandb: train/train_runtime 603.8881 wandb: train/train_samples_per_second 0.212 wandb: train/train_steps_per_second 0.007 wandb: wandb: 🚀 View run OpenAssistant/falcon-7b-sft-top1-696-falcon_log_7b-finetuned at: https://wandb.ai/toanbku/supervised-finetuning/runs/w1l8j7n6 wandb: Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s) wandb: Find logs at: ./wandb/run-20230717_162731-w1l8j7n6/logs [2023-07-17 16:40:50,566] [INFO] [launch.py:347:main] Process 46517 exits successfully. ```

asun17904/glue-qnli-t5-base

asun17904

pytorch

--- language: en license: mit library_name: pytorch --- # Plainly Optimized Network Dataset: GLUE Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 16 - `gradient_accumulation_steps` = 1 - `weight_decay` = 0.0 - `seed` = 42 |eval_loss|eval_accuracy|epoch| |--|--|--| |13.243|0.896|1.0| |12.807|0.909|2.0|

daniel40/e10aeb34-e512-499c-a0b6-7de0bce5c375

daniel40

peft

--- library_name: peft base_model: katuni4ka/tiny-random-olmo-hf tags: - axolotl - generated_from_trainer model-index: - name: e10aeb34-e512-499c-a0b6-7de0bce5c375 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: katuni4ka/tiny-random-olmo-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3c6bd5471e59572b_train_data.json ds_type: json format: custom path: /workspace/input_data/3c6bd5471e59572b_train_data.json type: field_input: text field_instruction: question field_output: answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/e10aeb34-e512-499c-a0b6-7de0bce5c375 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/3c6bd5471e59572b_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 18dd8258-3571-4446-974c-34d81c1c3d0a wandb_project: Birthday-SN56-28-Gradients-On-Demand wandb_run: your_name wandb_runid: 18dd8258-3571-4446-974c-34d81c1c3d0a warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e10aeb34-e512-499c-a0b6-7de0bce5c375 This model is a fine-tuned version of [katuni4ka/tiny-random-olmo-hf](https://huggingface.co/katuni4ka/tiny-random-olmo-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.7289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0004 | 1 | 10.8321 | | 10.7899 | 0.0186 | 50 | 10.7821 | | 10.7345 | 0.0371 | 100 | 10.7353 | | 10.7333 | 0.0557 | 150 | 10.7298 | | 10.7344 | 0.0743 | 200 | 10.7289 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

DUAL-GPO/zephyr-7b-gpo-v3-4-i2

DUAL-GPO

peft

--- library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: lole25/zephyr-7b-irepo-i1 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-gpo-v3-4-i2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-gpo-v3-4-i2 This model is a fine-tuned version of [lole25/zephyr-7b-irepo-i1](https://huggingface.co/lole25/zephyr-7b-irepo-i1) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

smarters/musicgen-small-csi

smarters

transformers

text-to-audio

--- inference: false tags: - musicgen license: cc-by-nc-4.0 duplicated_from: facebook/musicgen-small --- # MusicGen - Small - 300M MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: - [**small** (this checkpoint)](https://huggingface.co/facebook/musicgen-small) - [medium](https://huggingface.co/facebook/musicgen-medium) - [large](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main: ``` pip install git+https://github.com/huggingface/transformers.git ``` 2. Run the following Python code to generate text-conditional audio samples: ```py from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-small") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(**inputs, max_new_tokens=256) ``` 3. Listen to the audio samples either in an ipynb notebook: ```py from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```py import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("small") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details **Organization developing the model:** The FAIR team of Meta AI. **Model date:** MusicGen was trained between April 2023 and May 2023. **Model version:** This is the version 1 of the model. **Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. **Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284). **Citation details:** ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` **License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0. **Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use **Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs **Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. **Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics **Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. **Decision thresholds:** Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Evaluation results Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. | Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity | |---|---|---|---|---| | **facebook/musicgen-small** | 4.88 | 1.42 | 0.27 | - | | facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - | | facebook/musicgen-large | 5.48 | 1.37 | 0.28 | - | | facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 | More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section. ## Limitations and biases **Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. **Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). **Limitations:** - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. **Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. **Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. **Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.

Eurdem/Defne-llama3.1-8B

Eurdem

transformers

text-generation

--- license: llama3.1 language: - en - tr - de - fr - it - es library_name: transformers pipeline_tag: text-generation tags: - llama-3 - safetensors --- Fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct, trained on Turkish dataset (~4 mio tokens). Then it is merged with VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct. ## 💻 Kullanım/How to Use ```python !pip install -qU transformers bitsandbytes accelerate import transformers import torch model_id = "Eurdem/Defne-llama3.1-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16, "load_in_8bit": True}, device_map="auto", ) ## For English messages = [{"role": "system", "content": "You are a helpful chatbot, named Defne, who always responds friendly."}, {"role": "user", "content": "Answer the questions: 1) Who are you? 2) f(x)=3x^2+4x+12 so what is f(3)?"}, ] ## For Turkish messages = [{"role": "system", "content": "Sen, Defne isimli Türkçe konuşan bir chatbotsun."}, {"role": "user", "content": "Soruları numaralandırarak cevapla. 1) Sen kimsin? 2) f(x)=3x^2+4x+12 ise f(3) kaçtır?"} ] outputs = pipeline( messages, max_new_tokens=1024, do_sample=True, temperature=0.5, top_p=0.5, top_k=100, ) print(outputs[0]["generated_text"][-1]["content"]) ``` ### English Output ``` Hello there! I'm Defne, a friendly chatbot here to help you with any questions or tasks you might have. Now, let's answer your questions: 1. I am Defne, a helpful chatbot designed to provide friendly and informative responses to your queries. I'm always happy to assist you with anything you need help with! 2. To find f(3) for the given function f(x) = 3x^2 + 4x + 12, we simply need to substitute x with 3 in the function. f(3) = 3(3)^2 + 4(3) + 12 = 3(9) + 12 + 12 = 27 + 12 + 12 = 51 So, f(3) equals 51. ``` ### Türkçe Çıktı ``` 1) Ben Defne, Türkçe konuşan bir chatbotum. İnsanlarla sohbet etmek ve onlara yardımcı olmak için tasarlanmış bir yapay zekâ uygulamasıyım. 2) f(x) = 3x^2 + 4x + 12 formülüne x = 3 değerini koyarsak: f(3) = 3(3)^2 + 4(3) + 12 f(3) = 3(9) + 12 + 12 f(3) = 27 + 12 + 12 f(3) = 51 Sonuç olarak, f(3) = 51'dir. ```

pere/whisper-small-npsc

pere

transformers

automatic-speech-recognition

--- language: - nn license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-small-npsc results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: 16K_mp3_bokmaal split: train args: 16K_mp3_bokmaal metrics: - name: Wer type: wer value: 12.925418803583286 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-npsc This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2028 - Wer: 12.9254 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3922 | 0.18 | 500 | 0.3975 | 24.2055 | | 0.2893 | 0.36 | 1000 | 0.3139 | 20.1507 | | 0.2471 | 0.54 | 1500 | 0.2733 | 17.4449 | | 0.2159 | 0.72 | 2000 | 0.2488 | 16.2681 | | 0.2195 | 0.89 | 2500 | 0.2304 | 15.0577 | | 0.1178 | 1.07 | 3000 | 0.2245 | 14.5968 | | 0.1099 | 1.25 | 3500 | 0.2183 | 14.1118 | | 0.1059 | 1.43 | 4000 | 0.2136 | 13.7914 | | 0.1156 | 1.61 | 4500 | 0.2072 | 13.7491 | | 0.1025 | 1.79 | 5000 | 0.2034 | 13.1515 | | 0.1123 | 1.97 | 5500 | 0.2006 | 13.0284 | | 0.0734 | 2.15 | 6000 | 0.2028 | 12.9254 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

leo1200213/Finetuned_RADAR_tokenizer

leo1200213

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

brescia/kdrt_content_identification

brescia

transformers

text-classification

--- license: apache-2.0 base_model: indolem/indobertweet-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: kdrt_content_identification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kdrt_content_identification This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4682 - Accuracy: 0.855 - Precision: 0.855 - Recall: 0.855 - F1: 0.855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----:| | No log | 1.0 | 101 | 0.6326 | 0.805 | 0.805 | 0.805 | 0.805 | | No log | 2.0 | 202 | 0.4682 | 0.855 | 0.855 | 0.855 | 0.855 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ZurichNLP/mlit-llama-2-70b-mtml2

ZurichNLP

peft

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

thalllsssss/45a81f30-3d17-4b84-a45b-a2b51af00a14

thalllsssss

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 45a81f30-3d17-4b84-a45b-a2b51af00a14 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - be25ce38282aeb5a_train_data.json ds_type: json format: custom path: /workspace/input_data/be25ce38282aeb5a_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: thalllsssss/45a81f30-3d17-4b84-a45b-a2b51af00a14 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/be25ce38282aeb5a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 14fba03c-c528-4737-ac1e-1f62f6edce20 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 14fba03c-c528-4737-ac1e-1f62f6edce20 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 45a81f30-3d17-4b84-a45b-a2b51af00a14 This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.1782 | 0.0067 | 200 | 1.2583 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

r4zzchaudhary/tathyanka-nlq-depositandlending

r4zzchaudhary

transformers

text2text-generation

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: tathyanka-nlq-depositandlending results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tathyanka-nlq-depositandlending This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0049 - Rouge2 Precision: 0.8501 - Rouge2 Recall: 0.4035 - Rouge2 Fmeasure: 0.5465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | No log | 1.0 | 317 | 0.0420 | 0.8479 | 0.4017 | 0.5445 | | 0.6235 | 2.0 | 634 | 0.0144 | 0.8493 | 0.403 | 0.5459 | | 0.6235 | 3.0 | 951 | 0.0074 | 0.8493 | 0.403 | 0.5459 | | 0.0295 | 4.0 | 1268 | 0.0055 | 0.8497 | 0.4033 | 0.5462 | | 0.0166 | 5.0 | 1585 | 0.0049 | 0.8501 | 0.4035 | 0.5465 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

ivanma1/my_awesome_model

ivanma1

transformers

text-classification

--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5328 - Accuracy: 0.1944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 20 | 1.5233 | 0.1944 | | No log | 2.0 | 40 | 1.5328 | 0.1944 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

StarRing2022/RWKV-4-World-1.5B-Lora

StarRing2022

--- license: apache-2.0 --- 使用HF的接口很方便地对RWKV在Alpaca格式数据集基于Peft（注意版本为0.2）或RingPeft库 https://github.com/StarRing2022/ChatGPTX-Uni <br> 进行Lora增量微调及部署服务 底座模型：RWKV-4-World-1.5B（StarRing2022/RWKV-4-World-1.5B） 数据集：test.json，测试用 硬件设备：4090单卡，64G内存 训练轮数：100轮 训练耗时：5分钟左右 GIT开源地址：https://github.com/StarRing2022/HF-For-RWKVWorld-LoraAlpaca/

JVictor-CC/LLaMa-7b-Code-2.0

JVictor-CC

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2

mrHunghddddd/2e1cc897-aa26-4fca-b58c-f8e3c4571e94

mrHunghddddd

peft

--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-hf tags: - axolotl - generated_from_trainer model-index: - name: 2e1cc897-aa26-4fca-b58c-f8e3c4571e94 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: codellama/CodeLlama-7b-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 09a979ccd0b58c2c_train_data.json ds_type: json format: custom path: /workspace/input_data/09a979ccd0b58c2c_train_data.json type: field_input: '' field_instruction: content field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: mrHunghddddd/2e1cc897-aa26-4fca-b58c-f8e3c4571e94 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/09a979ccd0b58c2c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1e5e9609-ad29-4797-9b4d-ea610be96ec8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 1e5e9609-ad29-4797-9b4d-ea610be96ec8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 2e1cc897-aa26-4fca-b58c-f8e3c4571e94 This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.9387 | 0.7812 | 200 | 1.8611 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mlabonne/BeagleB-7B

mlabonne

transformers

text-generation

--- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit base_model: - mlabonne/OmniBeagle-7B - shadowml/BeagleX-7B - shadowml/FoxBeagle-7B --- # BeagleB-7B BeagleB-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) * [shadowml/BeagleX-7B](https://huggingface.co/shadowml/BeagleX-7B) * [shadowml/FoxBeagle-7B](https://huggingface.co/shadowml/FoxBeagle-7B) ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: mlabonne/OmniBeagle-7B parameters: density: 0.65 weight: 0.76 - model: shadowml/BeagleX-7B parameters: density: 0.6 weight: 0.12 - model: shadowml/FoxBeagle-7B parameters: density: 0.6 weight: 0.12 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/BeagleB-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

mradermacher/Furry_Request_3B-GGUF

mradermacher

transformers

--- base_model: jeiku/Furry_Request_3B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jeiku/Furry_Request_3B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q2_K.gguf) | Q2_K | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q3_K_S.gguf) | Q3_K_S | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q3_K_L.gguf) | Q3_K_L | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.IQ4_XS.gguf) | IQ4_XS | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q5_K_S.gguf) | Q5_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q5_K_M.gguf) | Q5_K_M | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q6_K.gguf) | Q6_K | 2.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Furry_Request_3B-GGUF/resolve/main/Furry_Request_3B.f16.gguf) | f16 | 5.7 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

brianflakes/deeprl-LunarLander-v2-PPO

brianflakes

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 296.18 +/- 13.50 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

devkyle/Akan-tiny-2000ms-1.5k

devkyle

transformers

automatic-speech-recognition

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-tiny-akan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-akan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2466 - Wer: 18.4363 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | 0.8631 | 2.9070 | 250 | 0.8110 | 57.7923 | | 0.4257 | 5.8140 | 500 | 0.7295 | 50.5545 | | 0.1859 | 8.7209 | 750 | 0.7882 | 50.0381 | | 0.0717 | 11.6279 | 1000 | 0.8608 | 49.2847 | | 0.0758 | 14.5349 | 1250 | 0.2162 | 17.7826 | | 0.0242 | 17.4419 | 1500 | 0.2390 | 19.1234 | | 0.0105 | 20.3488 | 1750 | 0.2467 | 19.7519 | | 0.0054 | 23.2558 | 2000 | 0.2466 | 18.4363 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF

mradermacher

transformers

--- base_model: PocketDoc/Dans-SakuraKaze-V1.0.0-12b datasets: - PocketDoc/Dans-Prosemaxx-Cowriter-3-S - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot - PocketDoc/Dans-Prosemaxx-InstructWriter-Continue - PocketDoc/Dans-Personamaxx-VN - PocketDoc/Dans-Personamaxx - PocketDoc/Dans-Personamaxx-Rainy - PocketDoc/Dans-Personamaxx-C1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/PocketDoc/Dans-SakuraKaze-V1.0.0-12b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/Dans-SakuraKaze-V1.0.0-12b-i1-GGUF/resolve/main/Dans-SakuraKaze-V1.0.0-12b.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

p1atdev/kakuyomu-genre-bert

p1atdev

transformers

text-classification

--- license: mit language: - ja library_name: transformers pipeline_tag: text-classification tags: - safetensors - bert widget: - example_title: 異世界ファンタジー text: 辺境貴族に転生したので現代知識活用して観光業始めます - example_title: SF text: メタバース・オンライン - example_title: ラブコメ text: 放課後、放送部の二人 - example_title: ミステリー text: タナカ・タロウの事件簿Ⅱ - example_title: 評論 text: 読みやすい文章の書き方とは？ --- # kakuyomu-genre-bert 小説のタイトルや紹介文からジャンルを分類する BERT 東北大の [cl-tohoku/bert-base-japanese-char-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v3) をベースにファインチューンされました。

nhungphammmmm/66c0261c-fc32-4fe4-bee3-dd445883d233

nhungphammmmm

peft

--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: 66c0261c-fc32-4fe4-bee3-dd445883d233 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e68ae5d029968e1f_train_data.json ds_type: json format: custom path: /workspace/input_data/e68ae5d029968e1f_train_data.json type: field_instruction: context field_output: answerA format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhungphammmmm/66c0261c-fc32-4fe4-bee3-dd445883d233 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/e68ae5d029968e1f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 330ded5e-8721-4c75-b6fe-d274b9663359 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 330ded5e-8721-4c75-b6fe-d274b9663359 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 66c0261c-fc32-4fe4-bee3-dd445883d233 This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.1365 | 0.1428 | 200 | 2.0583 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

prithivMLmods/Deepthink-Reasoning-7B

prithivMLmods

transformers

text-generation

--- license: creativeml-openrail-m language: - en base_model: - Qwen/Qwen2.5-7B-Instruct pipeline_tag: text-generation library_name: transformers tags: - code-solve - algorithm - codepy - qwen_base - 7b - CoT - deep-think --- <pre align="center"> .___ __ .__ .__ __ ____________. __| _/____ ____ _______/ |_| |__ |__| ____ | | __ \______ \_ |__ / __ |/ __ \_/ __ \\____ \ __\ | \| |/ \| |/ / / /| __ \ / /_/ \ ___/\ ___/| |_> > | | Y \ | | \ < / / | \_\ \ \____ |\___ >\___ > __/|__| |___| /__|___| /__|_ \ /____/ |___ / \/ \/ \/|__| \/ \/ \/ \/ </pre> The **Deepthink-Reasoning-7B** is a fine-tuned version of the **Qwen2.5-7B-Instruct** base model, designed for text generation tasks that require deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing. With its robust natural language processing capabilities, **Deepthink-Reasoning-7B** excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs. - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains. - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots. - **Long-context Support** up to 128K tokens and can generate up to 8K tokens. - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. # **Demo Start** Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Deepthink-Reasoning-7B" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` # **Run with Ollama [Ollama Run]** Ollama makes running machine learning models simple and efficient. Follow these steps to set up and run your GGUF models quickly. ## Quick Start: Step-by-Step Guide | Step | Description | Command / Instructions | |------|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | **Install Ollama 🦙** | Download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your system. | | 2 | **Create Your Model File** | - Create a file named after your model, e.g., `metallama`. | | | | - Add the following line to specify the base model: | | | | ```bash | | | | FROM Llama-3.2-1B.F16.gguf | | | | ``` | | | | - Ensure the base model file is in the same directory. | | 3 | **Create and Patch the Model** | Run the following commands to create and verify your model: | | | | ```bash | | | | ollama create metallama -f ./metallama | | | | ollama list | | | | ``` | | 4 | **Run the Model** | Use the following command to start your model: | | | | ```bash | | | | ollama run metallama | | | | ``` | | 5 | **Interact with the Model** | Once the model is running, interact with it: | | | | ```plaintext | | | | >>> Tell me about Space X. | | | | Space X, the private aerospace company founded by Elon Musk, is revolutionizing space exploration... | | | | ``` | ## Conclusion With Ollama, running and interacting with models is seamless. Start experimenting today!

hopkins/eng-fra-common

hopkins

transformers

translation

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: eng-fra-common results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eng-fra-common This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1327 - Bleu: 33.1235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

yujiepan/phi-3.5-tiny-random

yujiepan

transformers

text-generation

--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is for debugging. It is randomly initialized using the config from [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) but with smaller size. Codes: ```python import os import torch import transformers from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline, set_seed) model_id = "microsoft/Phi-3.5-mini-instruct" repo_id = "yujiepan/phi-3.5-tiny-random" save_path = f"/tmp/{repo_id}" config = AutoConfig.from_pretrained(model_id, trust_remote_code=True) config.hidden_size = 16 config.intermediate_size = 32 config.num_attention_heads = 4 config.num_hidden_layers = 2 config.num_key_value_heads = 4 config.rope_scaling['long_factor'] = [1.0299, 1.0499] config.rope_scaling['short_factor'] = [1.05, 1.05] tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) tokenizer.save_pretrained(save_path) model = AutoModelForCausalLM.from_config( config, torch_dtype=torch.bfloat16, # attn_implementation="sdpa", trust_remote_code=True, ) model.generation_config = GenerationConfig.from_pretrained( model_id, trust_remote_code=True ) set_seed(42) with torch.no_grad(): for _, p in sorted(model.named_parameters()): torch.nn.init.uniform_(p, -0.2, 0.2) model.save_pretrained(save_path) pipe = pipeline("text-generation", model=save_path, device="cuda", trust_remote_code=True, max_new_tokens=20) print(pipe("Hello World!")) ```

MarkBW/armor-samurai

MarkBW

diffusers

text-to-image

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\01\0w\0o\0m\0a\0n\0,\0 \0p\0o\0r\0t\0r\0a\0i\0t\0,\0 \0 \0a\0 \0b\0e\0a\0u\0t\0i\0f\0u\0l\0 \0g\0i\0r\0l\0 \0w\0e\0a\0r\0i\0n\0g\0 \0s\0a\0m\0u\0r\0a\0i\0 \0h\0e\0l\0m\0e\0t\0,\0 \0 \0s\0h\0o\0r\0t\0 \0h\0a\0i\0r\0,\0 \0l\0o\0o\0k\0i\0n\0g\0 \0a\0t\0 \0v\0i\0e\0w\0e\0r\0,\0 \0j\0a\0p\0a\0n\0 \0c\0a\0s\0t\0l\0e\0 \0i\0n\0 \0f\0r\0o\0n\0t\0 \0o\0f\0 \0f\0u\0l\0l\0 \0m\0o\0o\0n\0 \0c\0e\0n\0t\0e\0r\0 \0i\0n\0 \0f\0r\0a\0m\0e\0,\0 \0<\0l\0o\0r\0a\0:\0s\0a\0m\0u\0r\0a\0i\0L\0o\0r\0a\0V\00\01\0:\00\0.\08\0>\0,\0 \0P\0h\0o\0t\0o\0r\0e\0a\0l\0i\0s\0t\0i\0c\0,\0 \0H\0y\0p\0e\0r\0r\0e\0a\0l\0i\0s\0t\0i\0c\0,\0 \0H\0y\0p\0e\0r\0d\0e\0t\0a\0i\0l\0e\0d\0,\0 \0a\0n\0a\0l\0o\0g\0 \0s\0t\0y\0l\0e\0,\0 \0h\0i\0p\0 \0c\0o\0c\0k\0e\0d\0,\0 \0d\0e\0m\0u\0r\0e\0,\0 \0l\0o\0w\0 \0c\0u\0t\0,\0 \0d\0e\0t\0a\0i\0l\0e\0d\0 \0s\0k\0i\0n\0,\0 \0m\0a\0t\0t\0e\0 \0s\0k\0i\0n\0,\0 \0s\0o\0f\0t\0 \0l\0i\0g\0h\0t\0i\0n\0g\0,\0 \0s\0u\0b\0s\0u\0r\0f\0a\0c\0e\0 \0s\0c\0a\0t\0t\0e\0r\0i\0n\0g\0,\0 \0r\0e\0a\0l\0i\0s\0t\0i\0c\0,\0 \0h\0e\0a\0v\0y\0 \0s\0h\0a\0d\0o\0w\0,\0 \0m\0a\0s\0t\0e\0r\0p\0i\0e\0c\0e\0,\0 \0b\0e\0s\0t\0 \0q\0u\0a\0l\0i\0t\0y\0,\0 \0u\0l\0t\0r\0a\0 \0r\0e\0a\0l\0i\0s\0t\0i\0c\0,\0 \08\0k\0,\0 \0g\0o\0l\0d\0e\0n\0 \0r\0a\0t\0i\0o\0,\0 \0I\0n\0t\0r\0i\0c\0a\0t\0e\0,\0 \0H\0i\0g\0h\0 \0D\0e\0t\0a\0i\0l\0,\0 \0f\0i\0l\0m\0 \0p\0h\0o\0t\0o\0g\0r\0a\0p\0h\0y\0,\0 \0s\0o\0f\0t\0 \0f\0o\0c\0u\0s\0,\0 \0 \0b\0l\0u\0r\0r\0y\0 \0b\0a\0c\0k\0g\0r\0o\0u\0n\0d\0,\0" output: url: images/tmpgq75avu5.jpeg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: samurai, armor --- # armor-samurai <Gallery /> ## Model description Creates renders of Samurai armor by adhicipta ## Trigger words You should use `samurai` to trigger the image generation. You should use `armor` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/MarkBW/armor-samurai/tree/main) them in the Files & versions tab.

stablediffusionapi/xsmerge-realisticvisionv3

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # XSMerge-RealisticVisionV3-ForArchitectural API Inference  ## Get API Key Get API key from [ModelsLab](https://modelslab.com/), No Payment needed. Replace Key in below code, change **model_id** to "xsmerge-realisticvisionv3" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/xsmerge-realisticvisionv3) Model link: [View model](https://stablediffusionapi.com/models/xsmerge-realisticvisionv3) Credits: [View credits](https://civitai.com/?query=XSMerge-RealisticVisionV3-ForArchitectural) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "xsmerge-realisticvisionv3", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

PrunaAI/tf_efficientnetv2_l.in1k-turbo-green-smashed

PrunaAI

pruna-engine

--- library_name: pruna-engine thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [](https://twitter.com/PrunaAI) [](https://github.com/PrunaAI) [](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results  **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton. - ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 2. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir tf_efficientnetv2_l.in1k-turbo-green-smashed huggingface-cli download PrunaAI/tf_efficientnetv2_l.in1k-turbo-green-smashed --local-dir tf_efficientnetv2_l.in1k-turbo-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "tf_efficientnetv2_l.in1k-turbo-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "tf_efficientnetv2_l.in1k-turbo-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. import torch; image = torch.rand(1, 3, 224, 224).to('cuda') smashed_model(image) ``` ## Configurations The configuration info are in `model/smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model tf_efficientnetv2_l.in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

Rodo-Sami/80ff7ca0-696f-4273-a00e-0480be049e21

Rodo-Sami

peft

--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Math-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 80ff7ca0-696f-4273-a00e-0480be049e21 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.6.0` ```yaml adapter: lora base_model: Qwen/Qwen2.5-Math-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 2cb8b307cab6f131_train_data.json ds_type: json format: custom path: /workspace/input_data/2cb8b307cab6f131_train_data.json type: field_instruction: instruction field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: false group_by_length: true hub_model_id: Rodo-Sami/80ff7ca0-696f-4273-a00e-0480be049e21 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/2cb8b307cab6f131_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: offline wandb_name: b4c50b48-c554-4306-9e70-d47b8d7eaecf wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b4c50b48-c554-4306-9e70-d47b8d7eaecf warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 80ff7ca0-696f-4273-a00e-0480be049e21 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.0732 | 0.0608 | 375 | 2.2570 | | 2.7586 | 0.1216 | 750 | 2.1008 | | 2.0133 | 0.1823 | 1125 | 2.0228 | | 3.0612 | 0.2431 | 1500 | 2.0090 | ### Framework versions - PEFT 0.14.0 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

RayneAmes/adonis_v1

RayneAmes

transformers

text2text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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shisa-ai/shisa-v1-qwen2-7b

shisa-ai

transformers

text-generation

--- license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - generated_from_trainer model-index: - name: out results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: Qwen/Qwen2-7B-Instruct load_in_8bit: false load_in_4bit: false strict: false chat_template: chatml datasets: # This will be the path used for the data when it is saved to the Volume in the cloud. - path: augmxnt/ultra-orca-boros-en-ja-v1 ds_type: json type: sharegpt dataset_prepared_path: last_run_prepared val_set_size: 0.05 output_dir: ./out sequence_len: 8192 sample_packing: true pad_to_sequence_len: true neftune_noise_alpha: 5 use_wandb: true wandb_project: shisa-v2 wandb_entity: augmxnt wandb_name: shisa-v1-qwen2-7b gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: paged_adamw_8bit lr_scheduler: linear learning_rate: 8e-6 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 eval_per_epoch: 2 eval_table_size: saves_per_epoch: 0 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.01 fsdp: fsdp_config: special_tokens: pad_token: <|endoftext|> ``` </details><br> # out This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.8276 | 1.0196 | 319 | 0.5273 | | 0.6577 | 2.0164 | 637 | 0.5103 | | 0.5808 | 2.9541 | 936 | 0.5239 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

fakezeta/Meta-Llama-3-70B-Instruct-ov-int4

fakezeta

transformers

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE widget: - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <|end_of_text|> - <|eot_id|> --- # OpenVINO IR model with int4 quantization of llama-3-70B-Instruct Model definition for LocalAI: ``` name: llama3 backend: transformers parameters: model: fakezeta/meta-Llama-3-70B-Instruct-ov-int4 context_size: 8192 type: OVModelForCausalLM template: use_tokenizer_template: true stopwords: - "<|eot_id|>" - "<|end_of_text|>" ``` ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

bnunticha/lst20-sent-segment

bnunticha

transformers

token-classification

--- base_model: airesearch/wangchanberta-base-att-spm-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: lst20-sent-segment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lst20-sent-segment This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1513 - Precision: 0.7125 - Recall: 0.5426 - F1: 0.6160 - Accuracy: 0.9403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1842 | 1.0 | 1274 | 0.1596 | 0.6990 | 0.4995 | 0.5827 | 0.9361 | | 0.1622 | 2.0 | 2548 | 0.1513 | 0.7052 | 0.5330 | 0.6071 | 0.9389 | | 0.1554 | 3.0 | 3822 | 0.1513 | 0.7125 | 0.5426 | 0.6160 | 0.9403 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

driaforall/Tiny-Agent-a-0.5B-Q8-mlx

driaforall

mlx

--- license: apache-2.0 base_model: driaforall/Tiny-Agent-a-0.5B tags: - mlx --- # andthattoo/Tiny-Agent-a-0.5B-Q8-mlx The Model [andthattoo/Tiny-Agent-a-0.5B-Q8-mlx](https://huggingface.co/andthattoo/Tiny-Agent-a-0.5B-Q8-mlx) was converted to MLX format from [driaforall/Tiny-Agent-a-0.5B](https://huggingface.co/driaforall/Tiny-Agent-a-0.5B) using mlx-lm version **0.20.5**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("andthattoo/Tiny-Agent-a-0.5B-Q8-mlx") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

qizc/Phi-3-mini-4k-instruct-Q2_K-GGUF

qizc

text-generation

--- base_model: microsoft/Phi-3-mini-4k-instruct language: - en license: mit license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation tags: - nlp - code - llama-cpp - gguf-my-repo inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # qizc/Phi-3-mini-4k-instruct-Q2_K-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo qizc/Phi-3-mini-4k-instruct-Q2_K-GGUF --hf-file phi-3-mini-4k-instruct-q2_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo qizc/Phi-3-mini-4k-instruct-Q2_K-GGUF --hf-file phi-3-mini-4k-instruct-q2_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo qizc/Phi-3-mini-4k-instruct-Q2_K-GGUF --hf-file phi-3-mini-4k-instruct-q2_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo qizc/Phi-3-mini-4k-instruct-Q2_K-GGUF --hf-file phi-3-mini-4k-instruct-q2_k.gguf -c 2048 ```

renattissimo/vit-base-beans-demo-v5

renattissimo

transformers

image-classification

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-beans-demo-v5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0339 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0532 | 1.54 | 100 | 0.0339 | 0.9925 | | 0.0132 | 3.08 | 200 | 0.0465 | 0.9925 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview-4bit

mlx-community

mlx

--- base_model: FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview tags: - mlx --- # mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview-4bit The Model [mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview-4bit](https://huggingface.co/mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview-4bit) was converted to MLX format from [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview) using mlx-lm version **0.20.5**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview-4bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

tgoktug/my_awesome_bertsum_model

tgoktug

transformers

text2text-generation

--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_keras_callback model-index: - name: tgoktug/my_awesome_bertsum_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # tgoktug/my_awesome_bertsum_model This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8084 - Validation Loss: 0.7743 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0915 | 0.8645 | 0 | | 0.8934 | 0.8088 | 1 | | 0.8084 | 0.7743 | 2 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

Developer-Karthi/q-taxi-v3-rl

Developer-Karthi

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3-rl results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Developer-Karthi/q-taxi-v3-rl", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

peymansyh/whisper-tiny-en

peymansyh

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.358913813459268 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-en This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6260 - Wer Ortho: 0.3646 - Wer: 0.3589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 2.0491 | 3.57 | 50 | 1.0332 | 0.4670 | 0.4008 | | 0.294 | 7.14 | 100 | 0.5294 | 0.3646 | 0.3506 | | 0.0894 | 10.71 | 150 | 0.5465 | 0.3837 | 0.3636 | | 0.0163 | 14.29 | 200 | 0.6034 | 0.3757 | 0.3660 | | 0.0044 | 17.86 | 250 | 0.6260 | 0.3646 | 0.3589 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

lhong4759/d0e12197-07b2-40b3-a793-2d3cce83798f

lhong4759

peft

--- library_name: peft license: mit base_model: NousResearch/Nous-Hermes-llama-2-7b tags: - axolotl - generated_from_trainer model-index: - name: d0e12197-07b2-40b3-a793-2d3cce83798f results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Nous-Hermes-llama-2-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 77a6c7f6e0223ba0_train_data.json ds_type: json format: custom path: /workspace/input_data/77a6c7f6e0223ba0_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: lhong4759/d0e12197-07b2-40b3-a793-2d3cce83798f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/77a6c7f6e0223ba0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 80e14b82-7814-4596-8534-3041e5f0ad43 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 80e14b82-7814-4596-8534-3041e5f0ad43 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d0e12197-07b2-40b3-a793-2d3cce83798f This model is a fine-tuned version of [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.6635 | 0.1198 | 200 | 0.6727 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

smutuvi/whisper-small-sw-common-voice-ndizi-782

smutuvi

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: smutuvi/whisper-small-sw-common-voice model-index: - name: whisper-small-sw-common-voice-ndizi-782 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-sw-common-voice-ndizi-782 This model is a fine-tuned version of [smutuvi/whisper-small-sw-common-voice](https://huggingface.co/smutuvi/whisper-small-sw-common-voice) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9420 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0393 | 1.0 | 51 | 2.5562 | | 1.9426 | 2.0 | 102 | 2.4939 | | 1.9615 | 3.0 | 153 | 2.4289 | | 1.7863 | 4.0 | 204 | 2.3723 | | 1.8188 | 5.0 | 255 | 2.3275 | | 1.707 | 6.0 | 306 | 2.2926 | | 1.8352 | 7.0 | 357 | 2.2644 | | 1.6285 | 8.0 | 408 | 2.2394 | | 1.6887 | 9.0 | 459 | 2.2177 | | 1.6399 | 10.0 | 510 | 2.1987 | | 1.6828 | 11.0 | 561 | 2.1825 | | 1.5906 | 12.0 | 612 | 2.1671 | | 1.6641 | 13.0 | 663 | 2.1534 | | 1.6976 | 14.0 | 714 | 2.1405 | | 1.4269 | 15.0 | 765 | 2.1295 | | 1.4999 | 16.0 | 816 | 2.1192 | | 1.5153 | 17.0 | 867 | 2.1091 | | 1.5006 | 18.0 | 918 | 2.0997 | | 1.5664 | 19.0 | 969 | 2.0915 | | 1.4385 | 20.0 | 1020 | 2.0833 | | 1.4577 | 21.0 | 1071 | 2.0754 | | 1.3832 | 22.0 | 1122 | 2.0683 | | 1.4214 | 23.0 | 1173 | 2.0608 | | 1.3889 | 24.0 | 1224 | 2.0546 | | 1.3982 | 25.0 | 1275 | 2.0480 | | 1.4601 | 26.0 | 1326 | 2.0428 | | 1.4422 | 27.0 | 1377 | 2.0364 | | 1.3789 | 28.0 | 1428 | 2.0322 | | 1.3603 | 29.0 | 1479 | 2.0266 | | 1.5143 | 30.0 | 1530 | 2.0224 | | 1.3834 | 31.0 | 1581 | 2.0186 | | 1.338 | 32.0 | 1632 | 2.0134 | | 1.2825 | 33.0 | 1683 | 2.0110 | | 1.4324 | 34.0 | 1734 | 2.0066 | | 1.3716 | 35.0 | 1785 | 2.0032 | | 1.3713 | 36.0 | 1836 | 2.0000 | | 1.419 | 37.0 | 1887 | 1.9971 | | 1.3179 | 38.0 | 1938 | 1.9940 | | 1.3147 | 39.0 | 1989 | 1.9914 | | 1.4404 | 40.0 | 2040 | 1.9887 | | 1.2711 | 41.0 | 2091 | 1.9869 | | 1.2966 | 42.0 | 2142 | 1.9835 | | 1.2696 | 43.0 | 2193 | 1.9814 | | 1.3286 | 44.0 | 2244 | 1.9790 | | 1.271 | 45.0 | 2295 | 1.9766 | | 1.2839 | 46.0 | 2346 | 1.9746 | | 1.3299 | 47.0 | 2397 | 1.9734 | | 1.2858 | 48.0 | 2448 | 1.9711 | | 1.2222 | 49.0 | 2499 | 1.9697 | | 1.3 | 50.0 | 2550 | 1.9684 | | 1.305 | 51.0 | 2601 | 1.9664 | | 1.3894 | 52.0 | 2652 | 1.9644 | | 1.2221 | 53.0 | 2703 | 1.9635 | | 1.2858 | 54.0 | 2754 | 1.9632 | | 1.2739 | 55.0 | 2805 | 1.9615 | | 1.1671 | 56.0 | 2856 | 1.9606 | | 1.1928 | 57.0 | 2907 | 1.9590 | | 1.2995 | 58.0 | 2958 | 1.9576 | | 1.2287 | 59.0 | 3009 | 1.9572 | | 1.191 | 60.0 | 3060 | 1.9559 | | 1.2061 | 61.0 | 3111 | 1.9551 | | 1.1362 | 62.0 | 3162 | 1.9546 | | 1.155 | 63.0 | 3213 | 1.9537 | | 1.2597 | 64.0 | 3264 | 1.9528 | | 1.267 | 65.0 | 3315 | 1.9519 | | 1.1355 | 66.0 | 3366 | 1.9503 | | 1.2612 | 67.0 | 3417 | 1.9500 | | 1.2262 | 68.0 | 3468 | 1.9494 | | 1.2408 | 69.0 | 3519 | 1.9484 | | 1.2042 | 70.0 | 3570 | 1.9481 | | 1.1686 | 71.0 | 3621 | 1.9479 | | 1.2891 | 72.0 | 3672 | 1.9474 | | 1.3271 | 73.0 | 3723 | 1.9471 | | 1.3009 | 74.0 | 3774 | 1.9464 | | 1.1288 | 75.0 | 3825 | 1.9460 | | 1.2342 | 76.0 | 3876 | 1.9456 | | 1.2581 | 77.0 | 3927 | 1.9450 | | 1.0447 | 78.0 | 3978 | 1.9448 | | 1.2492 | 79.0 | 4029 | 1.9449 | | 1.2316 | 80.0 | 4080 | 1.9443 | | 1.0901 | 81.0 | 4131 | 1.9442 | | 1.1115 | 82.0 | 4182 | 1.9444 | | 1.1942 | 83.0 | 4233 | 1.9435 | | 1.1974 | 84.0 | 4284 | 1.9434 | | 1.2287 | 85.0 | 4335 | 1.9431 | | 1.1252 | 86.0 | 4386 | 1.9429 | | 1.0746 | 87.0 | 4437 | 1.9431 | | 1.1975 | 88.0 | 4488 | 1.9432 | | 1.2231 | 89.0 | 4539 | 1.9426 | | 1.1957 | 90.0 | 4590 | 1.9426 | | 1.1388 | 91.0 | 4641 | 1.9428 | | 1.198 | 92.0 | 4692 | 1.9426 | | 1.1479 | 93.0 | 4743 | 1.9423 | | 1.1635 | 94.0 | 4794 | 1.9423 | | 1.1184 | 95.0 | 4845 | 1.9422 | | 1.1971 | 96.0 | 4896 | 1.9421 | | 1.1907 | 97.0 | 4947 | 1.9418 | | 1.0373 | 98.0 | 4998 | 1.9419 | | 1.1927 | 99.0 | 5049 | 1.9421 | | 1.1475 | 100.0 | 5100 | 1.9420 | ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.1 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

anandshende/my_awesome_gptj_model

anandshende

transformers

question-answering

--- tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_gptj_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_gptj_model This model is a fine-tuned version of [hf-internal-testing/tiny-random-gptj](https://huggingface.co/hf-internal-testing/tiny-random-gptj) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 5.2705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.4517 | 1.0 | 2190 | 5.4139 | | 5.3181 | 2.0 | 4380 | 5.3079 | | 5.2604 | 3.0 | 6570 | 5.2705 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cpu - Datasets 2.12.0 - Tokenizers 0.13.3

PavelDanek/s2g_summ_bart

PavelDanek

transformers

summarization

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - PavelDanek/autotrain-data-skill2go_summ_mbart co2_eq_emissions: emissions: 5.638732652622368 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41524106867 - CO2 Emissions (in grams): 5.6387 ## Validation Metrics - Loss: 2.384 - Rouge1: 17.079 - Rouge2: 4.461 - RougeL: 16.808 - RougeLsum: 16.852 - Gen Len: 30.956 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/PavelDanek/autotrain-skill2go_summ_mbart-41524106867 ```

kk-aivio/b05ea90a-2a65-4ce7-a49f-a159f68c59e7

kk-aivio

peft

--- library_name: peft license: apache-2.0 base_model: dltjdgh0928/test_instruction tags: - axolotl - generated_from_trainer model-index: - name: b05ea90a-2a65-4ce7-a49f-a159f68c59e7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: dltjdgh0928/test_instruction bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 305ac52a66eb3533_train_data.json ds_type: json format: custom path: /workspace/input_data/305ac52a66eb3533_train_data.json type: field_input: context field_instruction: question field_output: answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kk-aivio/b05ea90a-2a65-4ce7-a49f-a159f68c59e7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/305ac52a66eb3533_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3c9585c0-7008-45d3-a888-729ace252e12 wandb_project: Birthday-SN56-17-Gradients-On-Demand wandb_run: your_name wandb_runid: 3c9585c0-7008-45d3-a888-729ace252e12 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b05ea90a-2a65-4ce7-a49f-a159f68c59e7 This model is a fine-tuned version of [dltjdgh0928/test_instruction](https://huggingface.co/dltjdgh0928/test_instruction) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0033 | 1 | nan | | 0.0 | 0.0100 | 3 | nan | | 0.0 | 0.0200 | 6 | nan | | 0.0 | 0.0300 | 9 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Ori/lama-2-13b-peft-strategyqa-no-retrieval-1-v2-seed-3

Ori

peft

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

cicimen/xlm-roberta-base-finetuned-panx-fr

cicimen

transformers

token-classification

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2685 - F1: 0.8387 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5754 | 1.0 | 191 | 0.3594 | 0.7624 | | 0.2657 | 2.0 | 382 | 0.2626 | 0.8199 | | 0.1812 | 3.0 | 573 | 0.2685 | 0.8387 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0

urkidi/Reinforce-PixelCopter

urkidi

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 38.30 +/- 22.65 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Hafad/ppo-LunarLander-v2-attempt1

Hafad

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.78 +/- 18.46 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF

mradermacher

transformers

--- base_model: igorktech/PODKATIK-qvikhr-2.5-1.5b language: - en library_name: transformers quantized_by: mradermacher tags: - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/igorktech/PODKATIK-qvikhr-2.5-1.5b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q2_K.gguf) | Q2_K | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q3_K_S.gguf) | Q3_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q3_K_L.gguf) | Q3_K_L | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q5_K_S.gguf) | Q5_K_S | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q5_K_M.gguf) | Q5_K_M | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q6_K.gguf) | Q6_K | 1.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/PODKATIK-qvikhr-2.5-1.5b-GGUF/resolve/main/PODKATIK-qvikhr-2.5-1.5b.f16.gguf) | f16 | 3.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

KZDADDY/Jenny-992

KZDADDY

transformers

text2text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

philip-hightech/a4ae87be-8cce-422b-96fd-939aaf1076f5

philip-hightech

peft

--- library_name: peft license: gemma base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo tags: - axolotl - generated_from_trainer model-index: - name: a4ae87be-8cce-422b-96fd-939aaf1076f5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 711eb262493f89e0_train_data.json ds_type: json format: custom path: /workspace/input_data/711eb262493f89e0_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: philip-hightech/a4ae87be-8cce-422b-96fd-939aaf1076f5 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 250 micro_batch_size: 2 mlflow_experiment_name: /tmp/711eb262493f89e0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 24cc9eb4-7f5e-4d72-a2ff-2c216f2efd51 wandb_project: Mine-SN56-21-Gradients-On-Demand wandb_run: your_name wandb_runid: 24cc9eb4-7f5e-4d72-a2ff-2c216f2efd51 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # a4ae87be-8cce-422b-96fd-939aaf1076f5 This model is a fine-tuned version of [zake7749/gemma-2-2b-it-chinese-kyara-dpo](https://huggingface.co/zake7749/gemma-2-2b-it-chinese-kyara-dpo) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 0.9216 | | 0.5381 | 0.0007 | 63 | 0.5674 | | 0.5471 | 0.0013 | 126 | 0.5365 | | 0.5064 | 0.0020 | 189 | 0.4999 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

HenryEnyi/lora_model

HenryEnyi

transformers

--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** HenryEnyi - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

jssky/8e723ac7-b09b-45fc-b5c4-5e44494f207e

jssky

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8e723ac7-b09b-45fc-b5c4-5e44494f207e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.6.0` ```yaml adapter: lora base_model: unsloth/SmolLM2-1.7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 19cbd10b0b31828d_train_data.json ds_type: json format: custom path: /workspace/input_data/19cbd10b0b31828d_train_data.json type: field_instruction: text field_output: code format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: true hub_model_id: jssky/8e723ac7-b09b-45fc-b5c4-5e44494f207e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/19cbd10b0b31828d_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 985ff6a6-dd6a-4648-b895-c172edabd3e7 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 985ff6a6-dd6a-4648-b895-c172edabd3e7 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8e723ac7-b09b-45fc-b5c4-5e44494f207e This model is a fine-tuned version of [unsloth/SmolLM2-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1942 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.2452 | 0.2313 | 375 | 0.2181 | | 0.1701 | 0.4627 | 750 | 0.2031 | | 0.2334 | 0.6940 | 1125 | 0.1959 | | 0.1953 | 0.9254 | 1500 | 0.1942 | ### Framework versions - PEFT 0.14.0 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

mradermacher/Quasar-2.5-7B-Ultra-GGUF

mradermacher

transformers

--- base_model: silx-ai/Quasar-2.5-7B-Ultra language: - en library_name: transformers model_name: Quasar-2.5-7B-Ultra quantized_by: mradermacher tags: - generated_from_trainer - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/silx-ai/Quasar-2.5-7B-Ultra <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q2_K.gguf) | Q2_K | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q5_K_M.gguf) | Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q6_K.gguf) | Q6_K | 6.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Quasar-2.5-7B-Ultra-GGUF/resolve/main/Quasar-2.5-7B-Ultra.f16.gguf) | f16 | 15.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

rsepulvedat/reliability_5W1H

rsepulvedat

--- license: apache-2.0 ---

qwp4w3hyb/DeepSeek-Coder-V2-Lite-Instruct-iMat-GGUF

qwp4w3hyb

--- license: other license_name: deepseek-license license_link: LICENSE base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct --- # Quant Infos - quants done with an importance matrix for improved quantization loss - ggufs & imatrix generated from bf16 for "optimal" accuracy loss - Wide coverage of different gguf quant types from Q\_8\_0 down to IQ1\_S - Quantized with [llama.cpp](https://github.com/ggerganov/llama.cpp) commit [4bfe50f741479c1df1c377260c3ff5702586719e](https://github.com/ggerganov/llama.cpp/commit/4bfe50f741479c1df1c377260c3ff5702586719e) (master as of 2024-06-11) - Imatrix generated with [this](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) multi-purpose dataset by [bartowski](https://huggingface.co/bartowski). ``` ./imatrix -c 512 -m $model_name-bf16.gguf -f calibration_datav3.txt -o $model_name.imatrix ``` # Original Model Card <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;"> <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;"> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="#4-api-platform">API Platform</a> | <a href="#5-how-to-run-locally">How to Use</a> | <a href="#6-license">License</a> | </p> <p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a> </p> # DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence ## 1. Introduction We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. <p align="center"> <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true"> </p> In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt). ## 2. Model Downloads We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public. <div align="center"> | **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** | | :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: | | DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) | | DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) | | DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) | | DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) | </div> ## 3. Chat Website You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in) ## 4. API Platform We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price. <p align="center"> <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true"> </p> ## 5. How to run locally **Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.** ### Inference with Huggingface's Transformers You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference. #### Code Completion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() input_text = "#write a quick sort algorithm" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` #### Code Insertion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() input_text = """<｜fim▁begin｜>def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[0] left = [] right = [] <｜fim▁hole｜> if arr[i] < pivot: left.append(arr[i]) else: right.append(arr[i]) return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>""" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):]) ``` #### Chat Completion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) # tokenizer.eos_token_id is the id of <|EOT|> token outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository. An example of chat template is as belows: ```bash <｜begin▁of▁sentence｜>User: {user_message_1} Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2} Assistant: ``` You can also add an optional system message: ```bash <｜begin▁of▁sentence｜>{system_message} User: {user_message_1} Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2} Assistant: ``` ### Inference with vLLM (recommended) To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650. ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams max_model_len, tp_size = 8192, 1 model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True) sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) messages_list = [ [{"role": "user", "content": "Who are you?"}], [{"role": "user", "content": "write a quick sort algorithm in python."}], [{"role": "user", "content": "Write a piece of quicksort code in C++."}], ] prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) generated_text = [output.outputs[0].text for output in outputs] print(generated_text) ``` ## 6. License This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use. ## 7. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).

srjv11/ppo-LunarLander-v2

srjv11

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.70 +/- 19.81 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

daniel40/072937c6-34d5-46b9-81d1-f7c90d0e6866

daniel40

peft

--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 072937c6-34d5-46b9-81d1-f7c90d0e6866 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b15e35925ee70791_train_data.json ds_type: json format: custom path: /workspace/input_data/b15e35925ee70791_train_data.json type: field_input: '' field_instruction: transcript field_output: summary format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/072937c6-34d5-46b9-81d1-f7c90d0e6866 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/b15e35925ee70791_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 90c0c5e7-f30c-472f-939a-bf80e344d7c0 wandb_project: Birthday-SN56-31-Gradients-On-Demand wandb_run: your_name wandb_runid: 90c0c5e7-f30c-472f-939a-bf80e344d7c0 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 072937c6-34d5-46b9-81d1-f7c90d0e6866 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6383 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0025 | 1 | 2.3971 | | 1.4512 | 0.1245 | 50 | 1.7998 | | 1.6961 | 0.2491 | 100 | 1.7253 | | 1.5053 | 0.3736 | 150 | 1.6695 | | 1.4271 | 0.4981 | 200 | 1.6383 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

balaramas/mbart_mt_entatrans

balaramas

transformers

text2text-generation

--- base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer model-index: - name: mbart_mt_entatrans results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart_mt_entatrans This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

smp-hub/timm-efficientnet-b3.advprop

smp-hub

segmentation-models-pytorch

image-classification

--- library_name: segmentation-models-pytorch license: other pipeline_tag: image-classification tags: - segmentation-models-pytorch - image-classification - pytorch - efficientnet languages: - python --- # Model card for timm-efficientnet-b3. This repository contains the `advprop` pre-trained weights for the `timm-efficientnet-b3` model used as encoder in the [segmentation-models-pytorch](https://github.com/qubvel-org/segmentation_models.pytorch) library. ### Example usage: 1. Install the library: ```bash pip install segmentation-models-pytorch ``` 2. Use the encoder in your code: ```python import segmentation_models_pytorch as smp model = smp.Unet("timm-efficientnet-b3", encoder_weights="advprop") ``` ### References - Github: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ - Original weights URL: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth

mukel/Qwen2.5-Math-7B-Instruct-GGUF

mukel

text-generation

--- base_model: Qwen/Qwen2.5-Math-7B-Instruct language: - en pipeline_tag: text-generation tags: - chat quantized_by: mukel license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE --- > [!Warning] > <div align="center"> > <b> > 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks. > </b> > </div> # GGUF models for qwen2.java Pure .gguf Q4_0 and Q8_0 quantizations of Qwen 2.5 models, ready to consume by `qwen2.java`. In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the token embeddings are quantized with Q6_K, instead of Q4_0. A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows: ``` ./llama-quantize --pure ./Qwen-2.5-7B-Instruct-BF16.gguf ./Qwen-2.5-7B-Instruct-Q4_0.gguf Q4_0 ``` ## Introduction In August 2024, we released the first series of mathematical LLMs - [Qwen2-Math](https://qwenlm.github.io/blog/qwen2-math/) - of our Qwen family. A month later, we have upgraded it and open-sourced **Qwen2.5-Math** series, including base models **Qwen2.5-Math-1.5B/7B/72B**, instruction-tuned models **Qwen2.5-Math-1.5B/7B/72B-Instruct**, and mathematical reward model **Qwen2.5-Math-RM-72B**. Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT.  While CoT plays a vital role in enhancing the reasoning capabilities of LLMs, it faces challenges in achieving computational accuracy and handling complex mathematical or algorithmic reasoning tasks, such as finding the roots of a quadratic equation or computing the eigenvalues of a matrix. TIR can further improve the model's proficiency in precise computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR. ## Model Details For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2.5-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2.5-Math).

RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Prima-LelantaclesV5-7b - GGUF - Model creator: https://huggingface.co/ChaoticNeutrals/ - Original model: https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV5-7b/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Prima-LelantaclesV5-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q2_K.gguf) | Q2_K | 2.53GB | | [Prima-LelantaclesV5-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [Prima-LelantaclesV5-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.IQ3_S.gguf) | IQ3_S | 2.96GB | | [Prima-LelantaclesV5-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [Prima-LelantaclesV5-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.IQ3_M.gguf) | IQ3_M | 3.06GB | | [Prima-LelantaclesV5-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q3_K.gguf) | Q3_K | 3.28GB | | [Prima-LelantaclesV5-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [Prima-LelantaclesV5-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [Prima-LelantaclesV5-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [Prima-LelantaclesV5-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q4_0.gguf) | Q4_0 | 3.83GB | | [Prima-LelantaclesV5-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [Prima-LelantaclesV5-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [Prima-LelantaclesV5-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q4_K.gguf) | Q4_K | 4.07GB | | [Prima-LelantaclesV5-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [Prima-LelantaclesV5-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q4_1.gguf) | Q4_1 | 4.24GB | | [Prima-LelantaclesV5-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q5_0.gguf) | Q5_0 | 4.65GB | | [Prima-LelantaclesV5-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [Prima-LelantaclesV5-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q5_K.gguf) | Q5_K | 4.78GB | | [Prima-LelantaclesV5-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [Prima-LelantaclesV5-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q5_1.gguf) | Q5_1 | 5.07GB | | [Prima-LelantaclesV5-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q6_K.gguf) | Q6_K | 5.53GB | | [Prima-LelantaclesV5-7b.Q8_0.gguf](https://huggingface.co/RichardErkhov/ChaoticNeutrals_-_Prima-LelantaclesV5-7b-gguf/blob/main/Prima-LelantaclesV5-7b.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- license: other library_name: transformers tags: - mergekit - merge base_model: - Test157t/Pasta-Lake-7b - Test157t/Prima-LelantaclesV4-7b-16k model-index: - name: Prima-LelantaclesV5-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.65 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV5-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.87 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV5-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV5-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 68.26 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV5-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV5-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 64.82 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV5-7b name: Open LLM Leaderboard --- Update: Getting suprisingly good results at 16384 context, which is unexpected given this context pool should remain untouched from other mistral models working around 8192.   Thanks to @Lewdiculus for the Quants: https://huggingface.co/Lewdiculous/Prima-LelantaclesV5-7b-GGUF This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method. The following models were included in the merge: * [Test157t/Pasta-Lake-7b](https://huggingface.co/Test157t/Pasta-Lake-7b) + [Test157t/Prima-LelantaclesV4-7b-16k](https://huggingface.co/Test157t/Prima-LelantaclesV4-7b-16k) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: dare_ties base_model: Test157t/Prima-LelantaclesV4-7b-16k parameters: normalize: true models: - model: Test157t/Pasta-Lake-7b parameters: weight: 1 - model: Test157t/Prima-LelantaclesV4-7b-16k parameters: weight: 1 dtype: float16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV5-7b) | Metric |Value| |---------------------------------|----:| |Avg. |73.09| |AI2 Reasoning Challenge (25-Shot)|70.65| |HellaSwag (10-Shot) |87.87| |MMLU (5-Shot) |64.52| |TruthfulQA (0-shot) |68.26| |Winogrande (5-shot) |82.40| |GSM8k (5-shot) |64.82|

ABDALLALSWAITI/DAVINCI

ABDALLALSWAITI

--- license: creativeml-openrail-m --- Year of Innovation: Monitoring and adapting to Civitai's evolving landscape. Comprehensive Model: Combines extensive training and elite models from various sources. Precision Enhancement: Utilizes multiple LoRA models for detailed improvements. Advanced Capabilities: Efficiently processes text, resolves hand depiction issues, interprets depth, and selects suitable colors for diverse art styles. Streamlined Experience: Developed multiple workflows for Comfy to simplify image creation. For simple prompts: Minimum of three steps. For complex descriptions: More steps are required. Workflow Link: For intuitive and efficient image creation guidance, refer to our detailed workflow. Adjust the CFG value and corresponding steps with care: increment the CFG by 0.1 for each additional step in the workflow, ensuring not to exceed a total of 5 CFG adjustments.

tensorblock/MegaBeam-Mistral-7B-512k-GGUF

tensorblock

--- license: apache-2.0 inference: false base_model: aws-prototyping/MegaBeam-Mistral-7B-512k tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## aws-prototyping/MegaBeam-Mistral-7B-512k - GGUF This repo contains GGUF format model files for [aws-prototyping/MegaBeam-Mistral-7B-512k](https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` <s>[INST] {prompt} [/INST] ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [MegaBeam-Mistral-7B-512k-Q2_K.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q2_K.gguf) | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes | | [MegaBeam-Mistral-7B-512k-Q3_K_S.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q3_K_S.gguf) | Q3_K_S | 2.947 GB | very small, high quality loss | | [MegaBeam-Mistral-7B-512k-Q3_K_M.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q3_K_M.gguf) | Q3_K_M | 3.277 GB | very small, high quality loss | | [MegaBeam-Mistral-7B-512k-Q3_K_L.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q3_K_L.gguf) | Q3_K_L | 3.560 GB | small, substantial quality loss | | [MegaBeam-Mistral-7B-512k-Q4_0.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q4_0.gguf) | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [MegaBeam-Mistral-7B-512k-Q4_K_S.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q4_K_S.gguf) | Q4_K_S | 3.856 GB | small, greater quality loss | | [MegaBeam-Mistral-7B-512k-Q4_K_M.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q4_K_M.gguf) | Q4_K_M | 4.068 GB | medium, balanced quality - recommended | | [MegaBeam-Mistral-7B-512k-Q5_0.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q5_0.gguf) | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [MegaBeam-Mistral-7B-512k-Q5_K_S.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q5_K_S.gguf) | Q5_K_S | 4.654 GB | large, low quality loss - recommended | | [MegaBeam-Mistral-7B-512k-Q5_K_M.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q5_K_M.gguf) | Q5_K_M | 4.779 GB | large, very low quality loss - recommended | | [MegaBeam-Mistral-7B-512k-Q6_K.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q6_K.gguf) | Q6_K | 5.534 GB | very large, extremely low quality loss | | [MegaBeam-Mistral-7B-512k-Q8_0.gguf](https://huggingface.co/tensorblock/MegaBeam-Mistral-7B-512k-GGUF/blob/main/MegaBeam-Mistral-7B-512k-Q8_0.gguf) | Q8_0 | 7.167 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/MegaBeam-Mistral-7B-512k-GGUF --include "MegaBeam-Mistral-7B-512k-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/MegaBeam-Mistral-7B-512k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

dmnkbckr/ppo-Huggy

dmnkbckr

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dmnkbckr/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Cnam-LMSSC/vibravox_phonemizers

Cnam-LMSSC

--- license: mit datasets: - Cnam-LMSSC/vibravox language: - fr --- # Master Model Card: Vibravox Speech-to-Phonemes Models <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" > </p> ## Overview This master model card serves as an entry point for exploring [multiple speech-to-phoneme models](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers#available-models) trained on different sensor data from the [Vibravox dataset](https://huggingface.co/datasets/Cnam-LMSSC/vibravox). These models are designed to convert French speech into sequences of International Phonetic Alphabet (IPA) encoded words, and are fine-tuned on specific sensors to address various audio capture scenarios using **body conducted** sound and vibration sensors. ## Disclaimer Each of these models has been trained for **specific non-conventional speech sensors** and is intended to be used with **in-domain data**. The only exception is the headset microphone phonemizer, which can certainly be used for many applications using audio data captured by airborne microphones. Please be advised that using these models outside their intended sensor data may result in suboptimal performance. ## Task Description The primary task for these models is an ASR task in the speech-to-phoneme context. Each model takes audio input and outputs a sequence of phonemes encoded in the IPA, facilitating precise phonetic transcription of French speech. Users unfamiliar with the phonetic alphabet can use tools like the [IPA reader](http://ipa-reader.xyz) to convert the transcript back to synthetic speech and evaluate the transcription quality. ## Usage All models are finetuned versions of [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2) and adapted to different sensor inputs. They are intended to be used at a sample rate of 16kHz. ## Training Procedure The models were each finetuned for 10 epochs with a constant learning rate of 1e-5. Detailed instructions for reproducing the experiments are available on the [jhauret/vibravox](https://github.com/jhauret/vibravox) Github repository and in the [VibraVox paper on arXiV](https://arxiv.org/abs/2407.11828). ## Available Models The following models are available, **each trained on a different sensor** on the `speech_clean` subset of (https://huggingface.co/datasets/Cnam-LMSSC/vibravox): | **Transducer** | **Huggingface model link** | |:---------------------------|:---------------------| | Reference headset microphone | [phonemizer_headset_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_headset_microphone) | | In-ear comply foam-embedded microphone |[phonemizer_soft_in_ear_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_soft_in_ear_microphone) | | In-ear rigid earpiece-embedded microphone | [phonemizer_rigid_in_ear_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_rigid_in_ear_microphone) | | Forehead miniature vibration sensor | [phonemizer_forehead_accelerometer](https://huggingface.co/Cnam-LMSSC/phonemizer_forehead_accelerometer) | | Temple vibration pickup | [phonemizer_temple_vibration_pickup](https://huggingface.co/Cnam-LMSSC/phonemizer_temple_vibration_pickup) | | Laryngophone | [phonemizer_throat_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_throat_microphone) |

mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF

mradermacher

transformers

--- base_model: prithivMLmods/Llama-3.1-8B-Open-SFT datasets: - O1-OPEN/OpenO1-SFT language: - en library_name: transformers license: creativeml-openrail-m quantized_by: mradermacher tags: - Chain-of-Thought Activation - Llama3.1 - 8B - CoT - SFT - text-generation-inference - Ollama - safetensors - Question Answering - Math --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/prithivMLmods/Llama-3.1-8B-Open-SFT <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Open-SFT-i1-GGUF/resolve/main/Llama-3.1-8B-Open-SFT.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

Dabe/dqn-LunarLander-v2-1

Dabe

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 75.22 +/- 112.40 name: mean_reward verified: false --- # **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

gptmurdock/classifier-main_subjects_religion

gptmurdock

transformers

text-classification

--- library_name: transformers tags: [] --- ## Fine-tuned roberta-base for detecting paragraphs on the topic of 'religion' ## Description This is a fine tuned roberta-base model for detecting whether paragraphs drawn from ethnographic source material are about 'religion'. ## Usage The easiest way to use this model at inference time is with the HF pipelines API. ```python from transformers import pipeline classifier = pipeline("text-classification", model="gptmurdock/classifier-main_subjects_religion") classifier("Example text to classify") ``` ## Training data ... ## Training procedure ... We use a 60-20-20 train-val-test split, and fine-tuned roberta-base for 5 epochs (lr = 2e-5, batch size = 40). ## Evaluation Evals on the test set are reported below. | Metric | Value | |-----------|-------| | Precision | 89.9 | | Recall | 90.2 | | F1 | 90.0 |

adeel300/Beast-Soul-new

adeel300

--- base_model: - udkai/Turdus - flemmingmiguel/MBX-7B tags: - merge - mergekit - lazymergekit - udkai/Turdus - flemmingmiguel/MBX-7B --- # Beast-Soul-new Beast-Soul-new is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [udkai/Turdus](https://huggingface.co/udkai/Turdus) * [flemmingmiguel/MBX-7B](https://huggingface.co/flemmingmiguel/MBX-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: udkai/Turdus layer_range: [0, 32] - model: flemmingmiguel/MBX-7B layer_range: [0, 32] merge_method: slerp base_model: udkai/Turdus parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "adeel300/Beast-Soul-new" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

XinnanZhang/pythia_tldr_ppo_1b_critic_44413

XinnanZhang

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

deepseek-ai/DeepSeek-Prover-V1.5-RL

deepseek-ai

--- license: other license_name: deepseek-license license_link: LICENSE base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;"> <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;"> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="#2-evaluation-results">Evaluation Results</a> | <a href="#3-model-downloads">Model Download</a> | <a href="#4-license">License</a> | <a href="#5-citation">Citation</a> | <a href="#6-contact">Contact</a> </p> <p align="center"> <a href="https://arxiv.org/abs/2408.08152"><b>Paper Link</b>👁️</a> </p> # DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search ## 1. Introduction We introduce DeepSeek-Prover-V1.5, an open-source language model designed for theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both training and inference processes. Pre-trained on DeepSeekMath-Base with specialization in formal mathematical languages, the model undergoes supervised fine-tuning using an enhanced formal theorem proving dataset derived from DeepSeek-Prover-V1. Further refinement is achieved through reinforcement learning from proof assistant feedback (RLPAF). Beyond the single-pass whole-proof generation approach of DeepSeek-Prover-V1, we propose RMaxTS, a variant of Monte-Carlo tree search that employs an intrinsic-reward-driven exploration strategy to generate diverse proof paths. DeepSeek-Prover-V1.5 demonstrates significant improvements over DeepSeek-Prover-V1, achieving new state-of-the-art results on the test set of the high school level miniF2F benchmark (63.5%) and the undergraduate level ProofNet benchmark (25.3%). <p align="center"> <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Prover-V1.5/blob/main/figures/performance.png?raw=true"> </p> ## 2. Evaluation Results <div align="center"> | | miniF2F-test | ProofNet | |--------|------------------|------------------| | **ReProver** | 26.5% | 13.8% | | **GPT-f** | 36.6% | - | | **Hypertree Proof Search** | 41.0% | - | | **InternLM2-StepProver** | 54.5% | 18.1% | | **DeepSeek-Prover-V1** | 50.0% | - | | **DeepSeek-Prover-V1.5-Base** | 42.2% | 13.2% | | **DeepSeek-Prover-V1.5-SFT** | 57.4% | 22.9% | | **DeepSeek-Prover-V1.5-RL** | 60.2% | 22.6% | | **DeepSeek-Prover-V1.5-RL + RMaxTS** | **63.5%** | **25.3%** | </div> ## 3. Model Downloads We release the DeepSeek-Prover-V1.5 with 7B parameters, including base, SFT and RL models, to the public. <div align="center"> | **Model** | **Download** | | :-----------------------------: | :----------------------------------------------------------: | | DeepSeek-Prover-V1.5-Base | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-Base) | | DeepSeek-Prover-V1.5-SFT | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT) | | DeepSeek-Prover-V1.5-RL | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-RL) | </div> ## 4. License This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use. See the [LICENSE-CODE](LICENSE-CODE) and [LICENSE-MODEL](LICENSE-MODEL) for more details. ## 5. Citation ```latex @article{xin2024deepseekproverv15harnessingproofassistant, title={DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search}, author={Huajian Xin and Z. Z. Ren and Junxiao Song and Zhihong Shao and Wanjia Zhao and Haocheng Wang and Bo Liu and Liyue Zhang and Xuan Lu and Qiushi Du and Wenjun Gao and Qihao Zhu and Dejian Yang and Zhibin Gou and Z. F. Wu and Fuli Luo and Chong Ruan}, year={2024}, eprint={2408.08152}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2408.08152}, } ``` ## 6. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).

emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8

emre

transformers

automatic-speech-recognition

--- license: apache-2.0 language: tr tags: - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - common_voice model-index: - name: wav2vec2-xls-r-300m-Tr-med-CommonVoice8 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice tr type: common_voice args: tr metrics: - name: Test WER type: wer value: 49.14 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-Tr-med-CommonVoice8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2556 - Wer: 0.4914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.4876 | 6.66 | 5000 | 0.3252 | 0.5784 | | 0.6919 | 13.32 | 10000 | 0.2720 | 0.5172 | | 0.5919 | 19.97 | 15000 | 0.2556 | 0.4914 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.1 - Tokenizers 0.10.3

mc1017/ppo-LunarLander-v2

mc1017

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: moon-lander results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.63 +/- 24.05 name: mean_reward verified: false --- # **moon-lander** Agent playing **LunarLander-v2** This is a trained model of a **moon-lander** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

SidXXD/resnet50_eps16_iter10_ddim50_t3-47600-bird-adv

SidXXD

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1*> bird tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/resnet50_eps16_iter10_ddim50_t3-47600-bird-adv These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> bird using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

LandCruiser/Tonasket_4

LandCruiser

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

LoneStriker/Skywork-13B-Spicyboros-3.1-6.0bpw-h6-exl2

LoneStriker

transformers

text-generation

--- license: other license_name: license license_link: >- https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf datasets: - jondurbin/airoboros-3.1 --- <!-- <div align="center"> <h1> ✨Skywork </h1> </div> --> <div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div> <p align="center"> 👨‍💻 <a href="https://github.com/SkyworkAI/Skywork" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a>• 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="http://arxiv.org/abs/2310.19341" target="_blank">Tech Report</a> </p> <div align="center"> [🎉天工在线对话平台已正式向公众开放](https://sso.tiangong.cn/?redirect=https://model-platform.tiangong.cn/overview&client_id=200005) </div> <div align="center"> [](https://github.com/SkyworkAI/Skywork/stargazers) [](https://github.com/SkyworkAI/Skywork/fork) </div> # 模型介绍（Introduction） **Skywork-13B-Base**模型在高质量清洗过滤的3.2万亿个多语言（主要是中文和英文）和代码数据上进行预训练，它在多种评测和各种基准测试上都展现了同等规模模型的最佳效果。 **Skywork-13B-Base**: The model was trained on a high-quality cleaned dataset consisting of 3.2 trillion multilingual data (mainly Chinese and English) and code. It has demonstrated the best performance among models of similar scale in various evaluations and benchmark tests. 如果您希望了解更多的信息，如训练方案，评估方法，请参考我们的[技术报告](http://arxiv.org/abs/2310.19341)，[Skymath](https://arxiv.org/abs/2310.16713)论文，[SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf)论文。 If you are interested in more training and evaluation details, please refer to our [technical report](http://arxiv.org/abs/2310.19341), [Skymath]((https://arxiv.org/skywork-tech-report)) paper and [SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf) paper. ## 训练数据（Training Data） 我们精心搭建了数据清洗流程对文本中的低质量数据、有害信息、敏感信息进行清洗过滤。我们的Skywork-13B-Base模型是在清洗后的3.2TB高质量中、英、代码数据上进行训练，其中英文占比52.2%，中文占比39.6%，代码占比8%，在兼顾中文和英文上的表现的同时，代码能力也能有保证。 We have developed a data cleaning pipeline with great care to effectively clean and filter low-quality data and eliminate harmful information from text data. Our Skywork-13B-Base model is trained on a dataset with 3.2TB tokens that consists of high-quality Chinese, English, and code data, all of which have been thoroughly cleaned. The English data comprises 52.2% of the dataset, the Chinese data accounts for 39.6%, and the code data makes up 8%. This comprehensive approach ensures optimal performance for both Chinese and English while also maintaining the ability to handle code. | | Category | Percentage | |-------------|------------------|------------| | **English** | Webpages | 39.8% | | | Books | 3.6% | | | Academic Papers | 3.0% | | | Encyclopedia | 0.5% | | | Miscellany | 2.9% | | **Chinese** | Webpages | 30.4% | | | Social Media | 5.5% | | | Encyclopedia | 0.8% | | | Miscellany | 3.1% | | **Other Lang.** | Encyclopedia | 2.4% | | **Code** | Github | 8.0% | ## 模型结构（Model Structure） 与Llama-2-13B模型对比，天工Skywork-13B模型采用相对更加瘦长的网络结构，层数为52层，同时将FFN Dim和Hidden Dim缩小到12288和4608，从而保证模型参数量和原始Llama-2-13B模型相当。根据我们前期实验对比，相对瘦长的网络结构在大Batch Size训练下可以取得更好的泛化效果。Skywork-13B和Llama-2-13B模型的对比如下： Compared to the Llama2-13B model, the Skywork-13B model adopts a relatively thinner and deeper network structure with 52 layers. At the same time, the FFN Dim and Hidden Dim are reduced to 12288 and 4608, respectively, to ensure that the model has a similar number of parameters as the original Llama-13B model. Based on our preliminary experimental results, a relatively thinner and deeper network structure can achieve better generalization performance under large batch size training. The detailed comparison between the Skywork-13B and Llama-2-13B models is as follows: | Model Structure | Llama2-13B | Skywork-13B | |----------------------|:----:|:-----------:| | Vocab. Size | 32,000 | 65,536 | | Hidden Dim. | 5,120 | 4,608 | | FFN Dim. | 13,696 | 12,288 | | Head Dim. | 128 | 128 | | Num. Heads | 40 | 36 | | Num. Layers | 40 | 52 | | Seq. Len. | 4,096 | 4,096 | | Positional Embedding | RoPE | RoPE | ## 分词器（Tokenizer） 我们使用Byte-Pair Encoding（BPE）对数据进行分词，词表大小为65536，其中拉丁字符和子词为32000个，汉字和Unicode符号8000个，汉语词语25519个，剩下的17个为保留字。 We use Byte-Pair Encoding (BPE) to tokenize the data, with a vocabulary size of 65536. Among them, there are 32000 Latin characters and subwords, 8000 Chinese characters and Unicode symbols, 25519 Chinese words, and the remaining 17 are reserved words. | Category | Size | |---------------------------------|--------| | Latin based words & subwords | 32000 | | Chinese characters & Unicode symbols | 8000 | | Chinese words | 25519 | | Reserved symbols | 17 | | **Total** | **65536** | # 模型评估（Evaluation） ## 领域数据困惑度评估（Perplexity Evaluaiton） 语言模型训练的本质上是让预测下一个词更准确。基于这个认知，我们认为评估基础大模型一个重要的方式是评估在各大领域上语言模型生成文章的概率。在模型训练中预测下一个词的概率一般使用Cross Entropy损失函数，整体的损失函数为每个位置预测真实词损失的平均，则有： $$loss = \sum^{n}_{i=1} log(p_i) / n = log( \prod_{i=1}^n p_i) / n$$ 其中$n$是文档的长度，即token数，$p_i$是位置i上真实词的概率，我们知道文档中每一个位置上真实词的概率的联乘则为生成该文档的概率，如此我们就将loss和生成文章的概率联系在了一起。而不同模型因为使用的分词器不同，具有不同的token数，因此对损失函数乘以token数目$n$，这样就仅考虑生成文章的概率部分，不同模型也可以进行比较。我们将标准化后loss取指数转换成perplexity，使得模型的差异更加可读。为了阅读方便后续提到的loss和ppl为模型标准化后的loss和perplexity。 基于上述分析，我们对对多个领域筛选出2023年9月份新发布的几百到上千篇高质量文章，并人工进行了核对。保证所有的测试数据不在天工模型以及其他所有模型的训练集中，并且测试数据的来源也足够广泛，质量也高。我们可以选取当前最新的文章评测不同模型的ppl，模型很难作弊。 下图列出了不同开源模型，天工Skywork-13B-Base取得最优效果，证明了我们的Base模型的基础能力处于国内开源模型中文最强水平。 We have chosen several hundred to thousands of high-quality articles that were published after September 1, 2023 across various fields. We have manually verified these articles to ensure their quality. It is important to note that none of the test data used in evaluating the Skywork model or any other models is included in their training set. Furthermore, the test data is diverse and of high quality, making it challenging for the models to gain an unfair advantage. The figure below displays the performance of different open source models. Skywork-13B-Base achieves the best results. | | Tech | Movie | Gov. | Game | Finance | General | Average | |------------------|-------|-------|-------|-------|---------|---------|---------| | MOSS-7B | 20.83 | 39.66 | 11.08 | 31.24 | 10.59 | 13.25 | 18.50 | | InternLM-7B | 13.43 | 24.90 | 5.88 | 19.78 | 6.17 | 8.10 | 11.17 | | Qwen-7B | 13.39 | 25.16 | 5.55 | 19.26 | 5.76 | 7.78 | 10.83 | | Baichuan2-7B | 12.89 | 23.26 | 5.34 | 18.36 | 5.68 | 7.62 | 10.41 | | LLaMA2-13B | 23.26 | 50.66 | 18.09 | 32.52 | 14.85 | 16.55 | 23.54 | | Xverse-13B | 12.55 | 23.49 | 5.20 | 17.69 | 5.54 | 7.46 | 10.19 | | Baichuan-13B | 12.38 | 22.46 | 5.21 | 17.59 | 5.42 | 7.37 | 10.03 | | Baichuan2-13B | 12.14 | 21.85 | 5.05 | 17.15 | 5.35 | 7.24 | 9.81 | | Qwen-14B | 11.90 | 22.43 | 4.89 | **16.94** | 5.24 | 7.03 | 9.67 | | InternLM-20B | 12.34 | 22.06 | 5.75 | 17.45 | 5.73 | 7.78 | 10.34 | | Aquila2-34B | 14.62 | 29.09 | 5.72 | 21.78 | 5.83 | 8.45 | 11.73 | | Skywork-13B-Base | **11.58** | **21.84** | **4.76** | 17.28 | **4.92** | **6.82** | **9.42** | ### 评测数据和评测脚本（Loss Evaluation） 我们将评测数据和评测脚本也进行了开源，下载github上的代码运行下面命令则可以复现我们的结果。 We have also open-sourced the data and evaluation scripts. You can reproduce our results by running the following command. ``` bash bash_scripts/skywork_eval_loss.sh ``` ## Benchmark评估（Benchmark Results） 我们评估了各大权威评测benchmark上的结果作为参考，包括C-Eval，MMLU，CMMLU，GSM8K。遵循之前的评估流程，C-Eval、MMLU、CMMLU测试5-shot结果，GSM8K测试8-shot结果。可以看到Skywork-13B-Base模型在中文开源模型中处于前列，在同等参数规模下为最优水平。 We evaluated Skywork-13B-Base on several popular benchmarks, including C-Eval, MMLU, CMMLU, and GSM8K. Following the previous evaluation process, we tested the 5-shot results of C-Eval, MMLU, and CMMLU, and the 8-shot results of GSM8K. It can be seen that the Skywork-13B-Base model is among the top models in the Chinese open source model community, performing at an optimal level with the same parameter scale. | Model | C-Eval | CMMLU | MMLU | GSM8K | |-------------------------|:-----:|:---------------:|:----------:|:-------:| | LLaMA-1-13B-Base | 35.5 | 31.2 | 46.9 | 17.8 | | Open-LLaMA-13B | 27.1 | 26.7 | 42.7 | 12.4 | | LLaMA-2-13B-Base | 36.5 | 36.6 | 54.8 | 28.7 | | InternLM-20B | 58.8 | - | 62.0 | 52.6 | | Qwen-14B-Base | 72.1 | 71.0 | 66.3 | 61.3 | | Aquila2-34B-Base | 63.1 | 71.4 | 64.2 | 58.4 | | XVERSE-13B-Base | 54.7 | - | 55.1 | - | | Baichuan-13B-Base | 52.4 | 55.3 | 51.6 | 26.6 | | Baichuan-2-13B-Base | 58.1 | 62.0 | 59.2 | 52.3 | | Skywork-13B-Base (ours) | 60.6 | 61.8 | 62.1 | 55.8 | ## Benchmark评估详细结果 我们给出**Skywork-13B-Base**模型在C-Eval，CMMLU，MMLU上模型的详细结果。 We provide detailed results of the Skywork-13B-Base model on C-EVAL, CMMLU, and MMLU. | Benchmark | **STEM** | **Humanities** | **Social Science** | **Other** | **China Specific** | **Hard** | **Average** | |:-----:|:---------:|:--------:|:-------------:|:--------:|:--------:|:--------:|:--------:| | **C-EVAL** | 51.2 | 67.8 | 74.6 | 57.5 | - | 39.4 | 60.6 | | **CMMLU** | 49.5 | 69.3 | 65.9 | 63.3 | 64.2 | - | 61.8 | | **MMLU** | 51.6 | 58.0 | 72.5 | 68.8 | - | - | 62.1 | # 快速开始（Quickstart） 我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。 We have open-sourced the model parameters, configuration files, tokenizer, and more on Huggingface and Modelscope. ## 依赖安装（Requirements） - Python 3.8及以上版本 - Pytorch 2.0及以上版本 - CUDA建议使用11.4以上版本。 Skywork-13B-Base模型，Skywork-13B-Chat模型和Skywork-13B-Math模型运行下面的脚本进行Python依赖安装。 - Python 3.8 and above - Pytorch 2.0 and above - CUDA 11.4 and above are recommended. Skywork-13B-Base model, Skywork-13B-Chat model, and Skywork-13B-Math model run the following script for Python dependency installation: ```shell pip install -r requirements.txt ``` ## Huggingface模型测试（Demonstration） ### Base 模型推理（Base Model Inference） ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> from transformers.generation import GenerationConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("SkyworkAI/Skywork-13B-Base", trust_remote_code=True) >>> model = AutoModelForCausalLM.from_pretrained("SkyworkAI/Skywork-13B-Base", device_map="auto", trust_remote_code=True).eval() >>> inputs = tokenizer('陕西的省会是西安', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，西安是我国著名的古都，在历史上有十三个朝代在此建都，所以西安又被称为“十三朝古都”。西安是我国著名的旅游城市，每年都有大量的游客来到西安旅游，西安的旅游资源非常丰富，有很多著名的旅游景点，比如秦始皇兵马俑、大雁塔、华清池、大唐芙蓉园、西安城墙、大明宫国家遗址公园、西安碑林博物馆、西安钟楼、西安鼓楼、西安半坡博物馆、西安大兴善寺、西安小雁塔 >>> inputs = tokenizer('陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州，湖北的省会是武汉，湖南的省会是长沙，江西的省会是南昌，安徽的省会是合肥，江苏的省会是南京，浙江的省会是杭州，福建的省会是福州，广东的省会是广州，广西的省会是南宁，海南的省会是海口，四川的省会是成都，贵州的省会是贵阳，云南的省会是昆明，西藏的省会是拉萨，青海的省会是西宁，宁夏的省会是银川，新疆的省会是乌鲁木齐。 ``` # 模型微调（Fine-tuning） ## 全量微调（Full-parameter Fine-tuning） 使用Skywork-13B-Base模型进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo ## launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt.sh ``` 使用Skywork-13B-Base模型进行有监督微调（SFT, Supevise Fine-tuning） ```bash ## preprocess data and launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft.sh ``` ## LoRA微调（PEFT） 使用Skywork-13B-Base模型以及LoRA进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt_lora.sh ``` 使用Skywork-13B-Base模型以及LoRA进行有监督微调（SFT, Supevise Fine-tuning） ```bash export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft_lora.sh ``` # 声明和协议（Declaration and License Agreement） ## 声明（Declaration） 我们在此声明，不要利用Skywork模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Skywork 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。 我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用skywork开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that the Skywork model should not be used for any activities that pose a threat to national or societal security or engage in unlawful actions. Additionally, we request users not to deploy the Skywork model for internet services without appropriate security reviews and records. We hope that all users will adhere to this principle to ensure that technological advancements occur in a regulated and lawful environment. We have done our utmost to ensure the compliance of the data used during the model's training process. However, despite our extensive efforts, due to the complexity of the model and data, there may still be unpredictable risks and issues. Therefore, if any problems arise as a result of using the Skywork open-source model, including but not limited to data security issues, public opinion risks, or any risks and problems arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 协议（License Agreement） 社区使用Skywork模型需要遵循[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)。Skywork模型支持商业用途，如果您计划将Skywork模型或其衍生品用于商业目的，无需再次申请， 但请您仔细阅读[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)并严格遵守相关条款。 The community usage of Skywork model requires [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). The Skywork model supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). [《Skywork 模型社区许可协议》》]:https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf [skywork-opensource@kunlun-inc.com]: mailto:skywork-opensource@kunlun-inc.com # 引用和联系我们（Contact Us and Citation） 如果您觉得我们的工作对您有帮助，欢迎引用我们的论文~ If you find our work helpful, please feel free to cite our paper~ ``` @misc{wei2023skywork, title={Skywork: A More Open Bilingual Foundation Model}, author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou}, year={2023}, eprint={2310.19341}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @article{skyworkmath, title={SkyMath: Technical Report}, author={Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu}, journal={arXiv preprint arXiv: 2310.16713}, url={https://arxiv.org/abs/2310.16713}, year={2023} } ``` ``` @article{Skywork_Multi-Modal_Group_Empirical_Study_Towards_2023, author = {Skywork Multi-Modal Group}, month = sep, title = {{Empirical Study Towards Building An Effective Multi-Modal Large Language Model}}, year = {2023} } ```

mradermacher/Llama3-ChatQA-1.5-8B-GGUF

mradermacher

transformers

--- base_model: nvidia/Llama3-ChatQA-1.5-8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - nvidia - chatqa-1.5 - chatqa - llama-3 - pytorch --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-8B-GGUF/resolve/main/Llama3-ChatQA-1.5-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

paulo037/stable-code-instruct-3b-spider2-1500-steps

paulo037

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mlabonne/gemma-2b-GGUF

mlabonne

transformers

--- library_name: transformers tags: [] extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- # Gemma-2B GGUF This is a quantized version of the [google/gemma-2b](https://huggingface.co/google/gemma-2b) model using [llama.cpp](https://github.com/ggerganov/llama.cpp). This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). **Model Page**: [Gemma](https://ai.google.dev/gemma/docs) **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) ## ⚡ Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_s`: Uses Q3_K for all tensors * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q4_k_s`: Uses Q4_K for all tensors * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q5_k_s`: Uses Q5_K for all tensors * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. ## 💻 Usage This model can be used with the latest version of llama.cpp and LM Studio >0.2.16.

sail-rvc/Sheen

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # Sheen ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:31:36 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

shubhamrathore081/entity_identification_v25

shubhamrathore081

transformers

text-generation

--- language: en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # My Model Card BASE_MODEL unsloth/Phi-3-mini-4k-instruct TRAINING_DATA train_modules_entities_6Sep.csv Training DateTime: 2024-09-07 14:09:49.357366 Training Objective: MAIN_ENTITY_EXTACTION HF Locaiton: shubhamrathore081/entity_identification_v25 Instruction format: Identify 'Main Entity', 'derived_from', and 'Intent Type' from the given statement Make sure to take 'derived_from' from the input statement only. These are the only possible intents: ['CREATE', 'UPDATE', 'LIST', 'ANALYTICS', 'UPLOAD'] Input: statement:{} JSON Response: {} Number of Samples: 10 Intent Accuracy: 0.0 Entity Accuracy: 0.0 Derived Synonyms Accuracy: 100.0

hoanghoavienvo/roberta-base-detect-depression-large-dataset-v3

hoanghoavienvo

transformers

text-classification

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-base-detect-depression-large-dataset-v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-detect-depression-large-dataset-v3 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6044 - Accuracy: 0.6918 - F1: 0.7921 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6532 | 1.0 | 876 | 0.5777 | 0.6527 | 0.7536 | | 0.6325 | 2.0 | 1752 | 0.5926 | 0.7322 | 0.8342 | | 0.6348 | 3.0 | 2628 | 0.5959 | 0.7433 | 0.8461 | | 0.635 | 4.0 | 3504 | 0.5781 | 0.7436 | 0.8449 | | 0.6177 | 5.0 | 4380 | 0.6044 | 0.6918 | 0.7921 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

stanfordnlp/stanza-ka

stanfordnlp

stanza

token-classification

--- tags: - stanza - token-classification library_name: stanza language: ka license: apache-2.0 --- # Stanza model for Georgian (ka) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2024-12-19 20:00:47.337

Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1308

Lots-of-LoRAs

pytorch

--- language: en license: mit library_name: pytorch --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1308 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task1308_amazonreview_category_classification - **Developed by:** bruel - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** LoRA - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/bruel-gabrielsson - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> https://huggingface.co/datasets/Lots-of-LoRAs/task1308_amazonreview_category_classification sourced from https://github.com/allenai/natural-instructions ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rishabhjain16/whisper-small-tcd-in

rishabhjain16

transformers

automatic-speech-recognition

--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - data_tcd_india metrics: - wer model-index: - name: Whisper Small TCD results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: data_tcd type: data_tcd_india args: 'config: english, split: test' metrics: - name: Wer type: wer value: 4.473391433339515 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small TCD This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the data_tcd dataset. It achieves the following results on the evaluation set: - Loss: 0.1792 - Wer: 4.4734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:------:| | 0.0144 | 3.4364 | 1000 | 0.1505 | 4.5337 | | 0.0012 | 6.8729 | 2000 | 0.1699 | 4.4641 | | 0.0004 | 10.3093 | 3000 | 0.1792 | 4.4734 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0

lesso08/8a91c468-d2b4-42f1-a320-1b4922ba5bbe

lesso08

peft

--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-3B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8a91c468-d2b4-42f1-a320-1b4922ba5bbe results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Llama-3.2-3B-Instruct bf16: true chat_template: llama3 datasets: - data_files: - e0c9da3c0d9db13f_train_data.json ds_type: json format: custom path: /workspace/input_data/e0c9da3c0d9db13f_train_data.json type: field_instruction: character field_output: statement format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso08/8a91c468-d2b4-42f1-a320-1b4922ba5bbe hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/e0c9da3c0d9db13f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3a6d8e3a-a716-4136-8561-b5805fd4063c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3a6d8e3a-a716-4136-8561-b5805fd4063c warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8a91c468-d2b4-42f1-a320-1b4922ba5bbe This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0007 | 1 | nan | | 0.0 | 0.0034 | 5 | nan | | 0.0 | 0.0069 | 10 | nan | | 0.0 | 0.0103 | 15 | nan | | 0.0 | 0.0137 | 20 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

davidschulte/ESM_muchocine_default

davidschulte

--- base_model: bert-base-multilingual-uncased datasets: - us-lsi/muchocine license: apache-2.0 tags: - embedding_space_map - BaseLM:bert-base-multilingual-uncased --- # ESM us-lsi/muchocine <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> ESM - **Developed by:** David Schulte - **Model type:** ESM - **Base Model:** bert-base-multilingual-uncased - **Intermediate Task:** us-lsi/muchocine - **ESM architecture:** linear - **Language(s) (NLP):** [More Information Needed] - **License:** Apache-2.0 license ## Training Details ### Intermediate Task - **Task ID:** us-lsi/muchocine - **Subset [optional]:** default - **Text Column:** review_body - **Label Column:** star_rating - **Dataset Split:** train - **Sample size [optional]:** 3872 - **Sample seed [optional]:** ### Training Procedure [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Language Model Training Hyperparameters [optional] - **Epochs:** 3 - **Batch size:** 32 - **Learning rate:** 2e-05 - **Weight Decay:** 0.01 - **Optimizer**: AdamW ### ESM Training Hyperparameters [optional] - **Epochs:** 10 - **Batch size:** 32 - **Learning rate:** 0.001 - **Weight Decay:** 0.01 - **Optimizer**: AdamW ### Additional trainiung details [optional] ## Model evaluation ### Evaluation of fine-tuned language model [optional] ### Evaluation of ESM [optional] MSE: ### Additional evaluation details [optional] ## What are Embedding Space Maps? <!-- This section describes the evaluation protocols and provides the results. --> Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text. ESMs can be used for intermediate task selection with the ESM-LogME workflow. ## How can I use Embedding Space Maps for Intermediate Task Selection? [](https://pypi.org/project/hf-dataset-selector) We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps. **hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub. ```python from hfselect import Dataset, compute_task_ranking # Load target dataset from the Hugging Face Hub dataset = Dataset.from_hugging_face( name="stanfordnlp/imdb", split="train", text_col="text", label_col="label", is_regression=False, num_examples=1000, seed=42 ) # Fetch ESMs and rank tasks task_ranking = compute_task_ranking( dataset=dataset, model_name="bert-base-multilingual-uncased" ) # Display top 5 recommendations print(task_ranking[:5]) ``` For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> If you are using this Embedding Space Maps, please cite our [paper](https://arxiv.org/abs/2410.15148). **BibTeX:** ``` @misc{schulte2024moreparameterefficientselectionintermediate, title={Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning}, author={David Schulte and Felix Hamborg and Alan Akbik}, year={2024}, eprint={2410.15148}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.15148}, } ``` **APA:** ``` Schulte, D., Hamborg, F., & Akbik, A. (2024). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. arXiv preprint arXiv:2410.15148. ``` ## Additional Information

BothBosu/bigru-caller-dialogue-scam-classifier-v1.0

BothBosu

transformers

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

shukdevdatta123/sql_injection_classifier_DeepSeek_R1_fine_tuned_model

shukdevdatta123

peft

--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit library_name: peft license: mit language: - en --- # Model Card for SQL Injection Classifier This model is designed to classify SQL queries as either normal (0) or as potential SQL injection attacks (1). ## Model Details ### Model Description This model is trained to identify SQL injection attacks, which are a type of code injection technique where an attacker can execute arbitrary SQL code in a database query. By analyzing the structure of SQL queries, the model predicts whether a given query is a normal query or contains malicious code indicative of an SQL injection attack. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** Fine-tuned Llama 8B model (Distilled Version) - **Language(s) (NLP):** English - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Colab Use To use the SQL Injection Classifier model, you can follow the code snippet below. This example demonstrates how to predict whether a given SQL query is normal or an injection attack. ```python # 1=sql injection query and 0=normal sql query from unsloth import FastLanguageModel from transformers import AutoTokenizer # Load the model and tokenizer model_name = "shukdevdatta123/sql_injection_classifier_DeepSeek_R1_fine_tuned_model" hf_token = "your hf tokens" model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_name, load_in_4bit=True, token=hf_token, ) # Function for testing queries def predict_sql_injection(query): # Prepare the model for inference inference_model = FastLanguageModel.for_inference(model) prompt = f"### Instruction:\nClassify the following SQL query as normal (0) or an injection attack (1).\n\n### Query:\n{query}\n\n### Classification:\n" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Use the inference model for generation outputs = inference_model.generate( input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, max_new_tokens=1000, use_cache=True, ) prediction = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] return prediction.split("### Classification:\n")[-1].strip() # Example usage test_query = "SELECT * FROM users WHERE id = '1' OR '1'='1' --" result = predict_sql_injection(test_query) print(f"Query: {test_query}\nPrediction: {result}") ``` ### Downstream Use This model can be integrated into applications requiring SQL injection detection, such as web application firewalls, database query analyzers, and security auditing tools. It can help identify and prevent potential vulnerabilities in SQL queries. ### Out-of-Scope Use This model should not be used for malicious purposes, such as testing vulnerabilities on unauthorized systems, or for making security decisions without human oversight. It is essential to understand that the model's predictions should be interpreted with caution and supplemented with additional security measures. ## Bias, Risks, and Limitations This model was trained on a dataset of SQL queries and may exhibit certain limitations: - **Bias**: The model may have limited generalization across different types of SQL injections or databases outside those present in the training set. - **Risks**: False positives or false negatives could lead to missed SQL injection attacks or incorrect identification of normal queries as injections. - **Limitations**: The model may not perform well on highly obfuscated attacks or queries that exploit novel vulnerabilities not present in the training data. ### Recommendations Users (both direct and downstream) should be aware of the potential risks of relying on the model in security-sensitive applications. Additional domain-specific testing and validation are recommended before deployment. ## How to Get Started with the Model (Colab Streamlit) ```python !pip install unsloth %%writefile app.py # 1=sql injection query and 0=normal sql query import streamlit as st from unsloth import FastLanguageModel from transformers import AutoTokenizer # Streamlit UI for input st.title("SQL Injection Classifier") hf_token = st.text_input("Enter your Hugging Face Token", type="password") model_name = "shukdevdatta123/sql_injection_classifier_DeepSeek_R1_fine_tuned_model" # Load the model and tokenizer when HF token is provided if hf_token: try: model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_name, load_in_4bit=True, token=hf_token, ) # Function for testing queries def predict_sql_injection(query): # Prepare the model for inference inference_model = FastLanguageModel.for_inference(model) prompt = f"### Instruction:\nClassify the following SQL query as normal (0) or an injection attack (1).\n\n### Query:\n{query}\n\n### Classification:\n" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Use the inference model for generation outputs = inference_model.generate( input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, max_new_tokens=1000, use_cache=True, ) prediction = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] return prediction.split("### Classification:\n")[-1].strip() # Input query from the user query = st.text_area("Enter an SQL query to test for injection", "") # Add a button to classify the query if st.button("Classify SQL Injection"): if query: result = predict_sql_injection(query) st.write(f"Prediction: {result}") else: st.write("Please enter a SQL query first.") except Exception as e: st.error(f"Error loading model: {str(e)}") else: st.write("Please enter your Hugging Face token to proceed.") !pip install streamlit !streamlit run app.py & npx localtunnel --port 8501 ``` ## Training Details ### Training Data The model was trained using a dataset of SQL queries, specifically focusing on SQL injection examples and normal queries. Each query is labeled as either normal (0) or an injection (1). ### Training Procedure The model was fine-tuned using the PEFT (Parameter Efficient Fine-Tuning) technique, optimizing a pre-trained Llama 8B model for the task of SQL injection detection. #### Training Hyperparameters - **Training regime:** Mixed precision (fp16). - **Learning rate:** 2e-4. - **Batch size:** 2 per device, with gradient accumulation steps of 4. - **Max steps:** 200. ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The evaluation was performed on a separate set of labeled SQL queries designed to test the model’s ability to differentiate between normal queries and SQL injection attacks. #### Metrics - **Accuracy:** How accurately the model classifies the queries. - **Precision and Recall:** Evaluating the model’s performance in detecting both true positives (injection attacks) and avoiding false positives. ### Results The model was evaluated based on the training loss across 200 steps. Below is the training loss progression during the training process: | Step | Training Loss | |------|---------------| | 10 | 2.951600 | | 20 | 1.572900 | | 30 | 1.370200 | | 40 | 1.081900 | | 50 | 0.946200 | | 60 | 1.028700 | | 70 | 0.873700 | | 80 | 0.793300 | | 90 | 0.892700 | | 100 | 0.863000 | | 110 | 0.694700 | | 120 | 0.685900 | | 130 | 0.778400 | | 140 | 0.748500 | | 150 | 0.721600 | | 160 | 0.714400 | | 170 | 0.764900 | | 180 | 0.750800 | | 190 | 0.664200 | | 200 | 0.700600 | #### Summary The model shows a significant reduction in training loss over the first 100 steps, indicating good convergence during the fine-tuning process. After step 100, the training loss becomes more stable but continues to fluctuate slightly. Overall, the model achieved a low loss by the final training step, suggesting effective learning and adaptation to the task of classifying SQL injections. ## Technical Specifications ### Model Architecture and Objective The model is based on a fine-tuned Llama 8B architecture, utilizing the PEFT technique to reduce the number of parameters required for fine-tuning while still maintaining good performance. ### Compute Infrastructure The model was trained using a powerful GPU cluster, leveraging mixed precision and gradient accumulation for optimal performance on large datasets. #### Hardware T4 GPU of Colab #### Software - **Libraries:** Hugging Face Transformers, unsloth, TRL, PyTorch. - **Training Framework:** PEFT. ## Glossary - **SQL Injection**: A type of attack where malicious SQL statements are executed in an application’s database. - **PEFT**: Parameter Efficient Fine-Tuning, a technique used for fine-tuning large models with fewer parameters. ## Model Card Authors [Shukdev Datta](https://www.linkedin.com/in/shukdev-datta-729767144/) ## Model Card Contact - **Email**: shukdevdatta@gmail.com - **GitHub**: [Click to here to access the Github Profile](https://github.com/shukdevtroy) - **WhatsApp**: [Click here to chat](https://wa.me/+8801719296601) ### Framework versions - PEFT 0.14.0

cuongdev/cuong-test

cuongdev

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### cuong-test Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

defog/sqlcoder2

defog

transformers

text-generation

--- license: other language: - en pipeline_tag: text-generation tags: - code --- # Defog SQLCoder Defog's SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries. [Interactive Demo](https://defog.ai/sqlcoder-demo/) | [🤗 HF Repo](https://huggingface.co/defog/sqlcoder2) | [♾️ Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7?usp=sharing) | [🐦 Twitter](https://twitter.com/defogdata) ## TL;DR SQLCoder is a 15B parameter model that outperforms `gpt-3.5-turbo` for natural language to SQL generation tasks on our [sql-eval](https://github.com/defog-ai/sql-eval) framework, and significantly outperforms all popular open-source models. When fine-tuned on a given schema, it also outperforms `gpt-4` SQLCoder is fine-tuned on a base StarCoder model. ## Results on novel datasets not seen in training | model | perc_correct | |-|-| | gpt4-2023-10-04 | 82.0 | | defog-sqlcoder2 | 77.5 | | gpt4-2023-08-28 | 74.0 | | defog-sqlcoder-7b | 71.0 | | gpt-3.5-2023-10-04 | 66.0 | | claude-2 | 64.5 | | gpt-3.5-2023-08-28 | 61.0 | | claude_instant_1 | 61.0 | | text-davinci-003 | 52.5 | ## License The code in this repo (what little there is of it) is Apache-2 licensed. The model weights have a `CC BY-SA 4.0` license, with additional responsible use restrictions added. The TL;DR is that you can use and modify the model for any purpose – including commercial use. However, if you modify the weights (for example, by fine-tuning), you must open-source your modified weights under the same license terms. ## Training Defog was trained on more than 20,000 human-curated questions. These questions were based on 10 different schemas. None of the schemas in the training data were included in our evaluation framework. You can read more about our [training approach](https://defog.ai/blog/open-sourcing-sqlcoder2-7b/) and [evaluation framework](https://defog.ai/blog/open-sourcing-sqleval/). ## Results by question category We classified each generated question into one of 5 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. | query_category | gpt-4 | sqlcoder2-15b | sqlcoder-7b | gpt-3.5 | claude-2 | claude-instant | gpt-3 | |:-----------------|--------:|----------------:|--------------:|----------:|-----------:|-----------------:|--------:| | date | 72 | 80 | 64 | 68 | 52 | 48 | 32 | | group_by | 91.4 | 82.9 | 82.9 | 77.1 | 71.4 | 71.4 | 71.4 | | order_by | 82.9 | 77.1 | 74.3 | 68.6 | 74.3 | 74.3 | 68.6 | | ratio | 80 | 74.3 | 54.3 | 37.1 | 57.1 | 45.7 | 25.7 | | join | 82.9 | 74.3 | 74.3 | 71.4 | 65.7 | 62.9 | 57.1 | | where | 80 | 77.1 | 74.3 | 74.3 | 62.9 | 60 | 54.3 | ## Using SQLCoder You can use SQLCoder via the `transformers` library by downloading our model weights from the Hugging Face repo. We have added sample code for [inference](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) on a [sample database schema](https://github.com/defog-ai/sqlcoder/blob/main/metadata.sql). ```bash python inference.py -q "Question about the sample database goes here" # Sample question: # Do we get more revenue from customers in New York compared to customers in San Francisco? Give me the total revenue for each city, and the difference between the two. ``` You can also use a demo on our website [here](https://defog.ai/sqlcoder-demo), or run SQLCoder in Colab [here](https://colab.research.google.com/drive/13BIKsqHnPOBcQ-ba2p77L5saiepTIwu0#scrollTo=ZpbVgVHMkJvC) ## Hardware Requirements SQLCoder has been tested on an A100 40GB GPU with `bfloat16` weights. You can also load an 8-bit and 4-bit quantized version of the model on consumer GPUs with 20GB or more of memory – like RTX 4090, RTX 3090, and Apple M2 Pro, M2 Max, or M2 Ultra Chips with 20GB or more of memory. ## Todo - [x] Open-source the v1 model weights - [x] Train the model on more data, with higher data variance - [ ] Tune the model further with Reward Modelling and RLHF - [ ] Pretrain a model from scratch that specializes in SQL analysis

DamianoPasquini/dqn-SpaceInvadersNoFrameskip-v

DamianoPasquini

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 654.00 +/- 236.45 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DamianoPasquini -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DamianoPasquini -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DamianoPasquini ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

zach-lamberty/mm2024-gpt-M-20240311T220716

zach-lamberty

transformers

text-generation

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: mm2024-gpt-M-20240311T220716 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mm2024-gpt-M-20240311T220716 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2

sergiocannata/convnext-tiny-224-finetuned-brs2

sergiocannata

transformers

image-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - f1 model-index: - name: convnext-tiny-224-finetuned-brs2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7924528301886793 - name: F1 type: f1 value: 0.7555555555555556 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnext-tiny-224-finetuned-brs2 This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2502 - Accuracy: 0.7925 - F1: 0.7556 - Precision (ppv): 0.8095 - Recall (sensitivity): 0.7083 - Specificity: 0.8621 - Npv: 0.7812 - Auc: 0.7852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:| | 0.6884 | 1.89 | 100 | 0.6907 | 0.5472 | 0.4286 | 0.5 | 0.375 | 0.6897 | 0.5714 | 0.5323 | | 0.5868 | 3.77 | 200 | 0.6604 | 0.6415 | 0.4242 | 0.7778 | 0.2917 | 0.9310 | 0.6136 | 0.6114 | | 0.4759 | 5.66 | 300 | 0.6273 | 0.6604 | 0.5 | 0.75 | 0.375 | 0.8966 | 0.6341 | 0.6358 | | 0.3599 | 7.55 | 400 | 0.6520 | 0.6604 | 0.5 | 0.75 | 0.375 | 0.8966 | 0.6341 | 0.6358 | | 0.3248 | 9.43 | 500 | 0.9115 | 0.6415 | 0.4571 | 0.7273 | 0.3333 | 0.8966 | 0.6190 | 0.6149 | | 0.3117 | 11.32 | 600 | 0.8608 | 0.6604 | 0.5263 | 0.7143 | 0.4167 | 0.8621 | 0.6410 | 0.6394 | | 0.4208 | 13.21 | 700 | 0.8774 | 0.6792 | 0.5641 | 0.7333 | 0.4583 | 0.8621 | 0.6579 | 0.6602 | | 0.5267 | 15.09 | 800 | 1.0131 | 0.6792 | 0.5405 | 0.7692 | 0.4167 | 0.8966 | 0.65 | 0.6566 | | 0.234 | 16.98 | 900 | 1.1498 | 0.6981 | 0.5556 | 0.8333 | 0.4167 | 0.9310 | 0.6585 | 0.6739 | | 0.7581 | 18.87 | 1000 | 1.0952 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 | | 0.1689 | 20.75 | 1100 | 1.1653 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 | | 0.0765 | 22.64 | 1200 | 1.1245 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 | | 0.6287 | 24.53 | 1300 | 1.2222 | 0.6981 | 0.6 | 0.75 | 0.5 | 0.8621 | 0.6757 | 0.6810 | | 0.0527 | 26.42 | 1400 | 1.2350 | 0.7358 | 0.6818 | 0.75 | 0.625 | 0.8276 | 0.7273 | 0.7263 | | 0.3622 | 28.3 | 1500 | 1.1022 | 0.7547 | 0.6667 | 0.8667 | 0.5417 | 0.9310 | 0.7105 | 0.7364 | | 0.3227 | 30.19 | 1600 | 1.1541 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 | | 0.3849 | 32.08 | 1700 | 1.2818 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 | | 0.4528 | 33.96 | 1800 | 1.3213 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 | | 0.1824 | 35.85 | 1900 | 1.3171 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 | | 0.0367 | 37.74 | 2000 | 1.4484 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 | | 0.07 | 39.62 | 2100 | 1.3521 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 | | 0.0696 | 41.51 | 2200 | 1.2636 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 | | 0.1554 | 43.4 | 2300 | 1.2225 | 0.7358 | 0.6667 | 0.7778 | 0.5833 | 0.8621 | 0.7143 | 0.7227 | | 0.2346 | 45.28 | 2400 | 1.2627 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 | | 0.097 | 47.17 | 2500 | 1.4892 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 | | 0.2494 | 49.06 | 2600 | 1.5282 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 | | 0.0734 | 50.94 | 2700 | 1.3989 | 0.7170 | 0.6341 | 0.7647 | 0.5417 | 0.8621 | 0.6944 | 0.7019 | | 0.1077 | 52.83 | 2800 | 1.5155 | 0.6792 | 0.5641 | 0.7333 | 0.4583 | 0.8621 | 0.6579 | 0.6602 | | 0.2456 | 54.72 | 2900 | 1.4400 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 | | 0.0823 | 56.6 | 3000 | 1.4511 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 | | 0.0471 | 58.49 | 3100 | 1.5114 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 | | 0.0144 | 60.38 | 3200 | 1.4412 | 0.7925 | 0.7317 | 0.8824 | 0.625 | 0.9310 | 0.75 | 0.7780 | | 0.1235 | 62.26 | 3300 | 1.2029 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 | | 0.0121 | 64.15 | 3400 | 1.4925 | 0.7358 | 0.6667 | 0.7778 | 0.5833 | 0.8621 | 0.7143 | 0.7227 | | 0.2126 | 66.04 | 3500 | 1.3614 | 0.7547 | 0.6667 | 0.8667 | 0.5417 | 0.9310 | 0.7105 | 0.7364 | | 0.0496 | 67.92 | 3600 | 1.2960 | 0.7736 | 0.7143 | 0.8333 | 0.625 | 0.8966 | 0.7429 | 0.7608 | | 0.1145 | 69.81 | 3700 | 1.3763 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 | | 0.1272 | 71.7 | 3800 | 1.6328 | 0.7170 | 0.5946 | 0.8462 | 0.4583 | 0.9310 | 0.675 | 0.6947 | | 0.0007 | 73.58 | 3900 | 1.5622 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 | | 0.0101 | 75.47 | 4000 | 1.1811 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 | | 0.0002 | 77.36 | 4100 | 1.8533 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 | | 0.0423 | 79.25 | 4200 | 1.2510 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 | | 0.0036 | 81.13 | 4300 | 1.3443 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 | | 0.0432 | 83.02 | 4400 | 1.2864 | 0.7736 | 0.7273 | 0.8 | 0.6667 | 0.8621 | 0.7576 | 0.7644 | | 0.0021 | 84.91 | 4500 | 0.8999 | 0.7925 | 0.7755 | 0.76 | 0.7917 | 0.7931 | 0.8214 | 0.7924 | | 0.0002 | 86.79 | 4600 | 1.3634 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 | | 0.0044 | 88.68 | 4700 | 1.7830 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 | | 0.0003 | 90.57 | 4800 | 1.2640 | 0.7736 | 0.7273 | 0.8 | 0.6667 | 0.8621 | 0.7576 | 0.7644 | | 0.0253 | 92.45 | 4900 | 1.2649 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 | | 0.0278 | 94.34 | 5000 | 1.7485 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 | | 0.1608 | 96.23 | 5100 | 1.2641 | 0.8113 | 0.7727 | 0.85 | 0.7083 | 0.8966 | 0.7879 | 0.8024 | | 0.0017 | 98.11 | 5200 | 1.6380 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 | | 0.001 | 100.0 | 5300 | 1.2502 | 0.7925 | 0.7556 | 0.8095 | 0.7083 | 0.8621 | 0.7812 | 0.7852 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

lukmanaj/vis-genome-fine-tuned-opus-mt-en-ha

lukmanaj

transformers

text2text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF

bartowski

text-generation

--- license: mit language: - en base_model: - unsloth/Phi-3-mini-4k-instruct datasets: - cognitivecomputations/Dolphin-2.9.2 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - internlm/Agent-FLAN - cognitivecomputations/SystemChat-2.0 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of dolphin-2.9.2-Phi-3-Medium-abliterated Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization. Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.2-Phi-3-Medium-abliterated All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q8_0.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q8_0.gguf) | Q8_0 | 14.83GB | Extremely high quality, generally unneeded but max available quant. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q6_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q6_K.gguf) | Q6_K | 11.45GB | Very high quality, near perfect, *recommended*. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q5_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q5_K_M.gguf) | Q5_K_M | 9.88GB | High quality, *recommended*. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q5_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q5_K_S.gguf) | Q5_K_S | 9.62GB | High quality, *recommended*. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q4_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q4_K_M.gguf) | Q4_K_M | 8.40GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q4_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q4_K_S.gguf) | Q4_K_S | 7.95GB | Slightly lower quality with more space savings, *recommended*. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ4_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ4_XS.gguf) | IQ4_XS | 7.50GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q3_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q3_K_L.gguf) | Q3_K_L | 7.34GB | Lower quality but usable, good for low RAM availability. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q3_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q3_K_M.gguf) | Q3_K_M | 6.75GB | Even lower quality. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ3_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ3_M.gguf) | IQ3_M | 6.29GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q3_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q3_K_S.gguf) | Q3_K_S | 6.06GB | Low quality, not recommended. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ3_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ3_XS.gguf) | IQ3_XS | 5.78GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ3_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ3_XXS.gguf) | IQ3_XXS | 5.41GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-Q2_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-Q2_K.gguf) | Q2_K | 5.20GB | Very low quality but surprisingly usable. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ2_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ2_M.gguf) | IQ2_M | 4.78GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ2_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ2_S.gguf) | IQ2_S | 4.40GB | Very low quality, uses SOTA techniques to be usable. | | [dolphin-2.9.2-Phi-3-Medium-abliterated-IQ2_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/blob/main/dolphin-2.9.2-Phi-3-Medium-abliterated-IQ2_XS.gguf) | IQ2_XS | 4.19GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF --include "dolphin-2.9.2-Phi-3-Medium-abliterated-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF --include "dolphin-2.9.2-Phi-3-Medium-abliterated-Q8_0.gguf/*" --local-dir dolphin-2.9.2-Phi-3-Medium-abliterated-Q8_0 ``` You can either specify a new local-dir (dolphin-2.9.2-Phi-3-Medium-abliterated-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski