initial commit of Jeopardy bot model
Browse files- README.md +80 -0
- config.json +24 -0
- configs/jeopardy-bot.yml +58 -0
- generation_config.json +7 -0
- pytorch_model-00001-of-00002.bin +3 -0
- pytorch_model-00002-of-00002.bin +3 -0
- pytorch_model.bin.index.json +330 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +33 -0
README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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datasets:
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- jeopardy
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language:
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- en
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---
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# Jeopardy Bot
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Jeopardy Bot is a reasobably good and fast bot at answering jeopardy questsions. Jeopardy is a great format for Language Models because the query is typically very short and the answer is typically even shorter.
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The `https://huggingface.co/datasets/jeopardy` dataset used to train this model is relatively incomplete with ~216K rows, but provides a rich evaluation set of data as https://j-archive.com/ provides daily updated clues and answers totaling ~468K clues and answers.
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# Sample Responses
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Here are some sampled questsions taken from recent episodes. We use Daily Doubles since those are often considered "harder".
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```
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Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.
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### Instruction:
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In 2023 16th St. Baptist in this city marks 150 years of the congregation, a history marred by a 1963 bombing
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### Input:
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Take me to church
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### Response:
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what is Birmingham
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```
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Correct! Daily Double! https://j-archive.com/suggestcorrection.php?clue_id=461177
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```
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Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.
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### Instruction:
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You can wear your Crocs as you see the gators in this national park established in 1947 at Florida's southwestern tip
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### Input:
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Florida Places
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### Response:
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what is Everglades National Park
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```
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Correct! Daily Double! https://j-archive.com/suggestcorrection.php?clue_id=461065
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Let's Just give it the Basic Alpaca prompt with no input or even a category...
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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On May 10-11, 1927 he flew from San Diego to New York City, with an overnight stop in St. Louis
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### Response:
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what is Charles Lindbergh
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```
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Correct! Daily Double! https://j-archive.com/suggestcorrection.php?clue_id=459838
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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He was born March 30, 1970, in a Virginia stable
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### Response:
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what is George Washington
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```
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Womp womp womp! Let's give it the category inline...
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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He was born March 30, 1970, in a Virginia stable. The Category is Sports GOATs.
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### Response:
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what is Michael Jordan
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```
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Well, even large language models aren't going to get these right all the time.
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# Training
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This model is SFT on LLaMa 7B. The training hyperparameters are provided in the YML config, which can be used directly with Axolotl. Training was stopped after ~2 epochs as the evaluation loss had started to increase after that. See https://wandb.ai/wing-lian/jeopardy-bot-7b
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config.json
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{
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"_name_or_path": "jeopardy_bot",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"max_sequence_length": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.29.0.dev0",
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"use_cache": false,
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"vocab_size": 32000
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}
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configs/jeopardy-bot.yml
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base_model: ./jeopardy-bot-7b
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base_model_config: huggyllama/llama-7b
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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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datasets:
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- path: openaccess-ai-collective/jeopardy
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type: jeopardy
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01
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adapter:
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lora_model_dir:
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sequence_len: 2048
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max_packed_sequence_len: 2048
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lora_r: 8
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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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- q_proj
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- v_proj
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lora_fan_in_fan_out: false
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wandb_project: jeopardy-bot-7b
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wandb_watch:
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wandb_run_id:
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wandb_log_model: checkpoint
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output_dir: ./jeopardy-bot-7b
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batch_size: 4
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micro_batch_size: 1
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num_epochs: 3
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optimizer: adamw_bnb_8bit
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torchdistx_path:
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lr_scheduler: cosine
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learning_rate: 0.0000002
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train_on_inputs: false
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group_by_length: false
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bf16: true
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tf32: 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: 5
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xformers_attention: true
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flash_attention:
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gptq_groupsize:
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gptq_model_v1:
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warmup_steps: 20
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eval_steps: 110
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save_steps: 660
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debug:
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deepspeed:
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weight_decay: 0.0001
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: "[PAD]"
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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.29.0.dev0"
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}
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pytorch_model-00001-of-00002.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:08c06027037b8ef4b212a8eabdd99eaefe633415f0ad2614a07c79c82e26fff2
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size 9976637886
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pytorch_model-00002-of-00002.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f3c4a21e9ffc57000d51b53e9dc666ec5874c0c4b5073b54cf007e83e7ba99f2
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size 3500316627
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pytorch_model.bin.index.json
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{
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"metadata": {
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"total_size": 13476839424
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},
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