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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
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model-index: |
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- name: qlora-out |
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results: [] |
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datasets: |
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- totally-not-an-llm/ZorgonChat |
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--- |
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[<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) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
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model_type: LlamaForCausalLM |
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tokenizer_type: LlamaTokenizer |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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datasets: |
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- path: totally-not-an-llm/ZorgonChat |
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type: alpaca |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./qlora-out |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 4096 |
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sample_packing: false |
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pad_to_sequence_len: true |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 4 |
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optimizer: paged_adamw_32bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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``` |
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</details><br> |
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# qlora-out |
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the [ZorgonChat](https://huggingface.co/datasets/totally-not-an-llm/ZorgonChat) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3466 |
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## Model description |
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Trained on a dataset of "alien language" chats to see if it will learn to talk in english. Prompt format is: |
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``` |
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You are a helpful assistant., respond in Language: English |
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### Instruction: |
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{prompt} |
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### Response: |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 3.9295 | 0.03 | 1 | 3.9073 | |
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| 3.5364 | 0.25 | 8 | 3.6199 | |
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| 3.263 | 0.5 | 16 | 3.1821 | |
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| 2.798 | 0.75 | 24 | 2.8962 | |
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| 2.7787 | 1.0 | 32 | 2.6773 | |
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| 2.5959 | 1.25 | 40 | 2.5506 | |
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| 2.4793 | 1.5 | 48 | 2.4955 | |
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| 2.5221 | 1.75 | 56 | 2.4613 | |
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| 2.4384 | 2.0 | 64 | 2.4055 | |
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| 2.295 | 2.25 | 72 | 2.3923 | |
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| 2.3943 | 2.5 | 80 | 2.3862 | |
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| 2.2398 | 2.75 | 88 | 2.3605 | |
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| 2.2693 | 3.0 | 96 | 2.3526 | |
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| 2.425 | 3.25 | 104 | 2.3471 | |
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| 2.2857 | 3.5 | 112 | 2.3468 | |
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| 2.2448 | 3.75 | 120 | 2.3451 | |
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| 2.1836 | 4.0 | 128 | 2.3466 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
