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--- |
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datasets: |
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- ehartford/dolphin |
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- LinhDuong/chatdoctor-200k |
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- sahil2801/code_instructions_120k |
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- c-s-ale/dolly-15k-instruction-alpaca-format |
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- tiiuae/falcon-refinedweb |
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- bigcode/starcoderdata |
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- togethercomputer/RedPajama-Data-1T |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- instruct |
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- medical |
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- code |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is an instruction-tuned LLaMa model with 33B parameters, with specialities in medical QA and code instruction. |
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## Model Details |
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<!-- Provide a longer summary of what this model is. --> |
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- **Model type:** LlamaForCausalLM |
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- **Language(s) (NLP):** English |
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- **License:** As a Llama-derivative, this model cannot be used commercially. |
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- **Finetuned from model (QLoRA):** [huggyllama/llama-30b](https://huggingface.co/huggyllama/llama-30b) |
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## Training Details |
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### Training Data |
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Converted the following datasets to alpaca:instruction format. |
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1. [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) |
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- ORCA style dataset generously created by [Eric Hartford](https://huggingface.co/ehartford) |
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2. [LinhDuong/chatdoctor-200k](https://huggingface.co/datasets/LinhDuong/chatdoctor-200k) |
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- Refined dataset sourced from icliniq medical QA forum |
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3. [sahil2801/code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k) |
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- Code instruction dataset generously created by Sahil Chaudhary from ThreeSixty AI |
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4. [c-s-ale/dolly-15k-instruction-alpaca-format](https://huggingface.co/datasets/c-s-ale/dolly-15k-instruction-alpaca-format) |
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- Dolly 15k is a general instruction dataset generated by employees of Databricks. |
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### Training Procedure |
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Trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) QLoRa on [RunPod](https://www.runpod.io/console/gpu-cloud) 8x A6000 on Community Cloud for 1 epochs (~23 hours - ~$110). |
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<details> |
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<summary>axolotl training config:</summary> |
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```yaml |
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base_model: huggyllama/llama-30b |
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base_model_config: huggyllama/llama-30b |
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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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push_dataset_to_hub: |
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hub_model_id: |
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hf_use_auth_token: |
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datasets: |
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- path: ehartford/dolphin |
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type: alpaca |
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data_files: |
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- flan1m-alpaca-uncensored.jsonl |
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- flan5m-alpaca-uncensored.jsonl |
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shards: 25 |
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- path: sahil2801/code_instructions_120k |
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type: alpaca |
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- path: LinhDuong/chatdoctor-200k |
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type: alpaca |
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shards: 2 |
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- path: c-s-ale/dolly-15k-instruction-alpaca-format |
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type: alpaca |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.01 |
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adapter: qlora |
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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: 32 |
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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_mode: true |
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wandb_project: med-orca-instruct-33b |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: 'openllama_checkpoint' |
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output_dir: /disk/med-instruct-33b |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 4 |
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num_epochs: 1 |
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optimizer: paged_adamw_32bit |
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torchdistx_path: |
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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: true |
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fp16: false |
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tf32: true |
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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: 2 |
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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: 100 |
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eval_steps: 20 |
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save_steps: |
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debug: |
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deepspeed: true |
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weight_decay: 0.00001 |
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special_tokens: |
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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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``` |
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</details> |
