peft safetensors llama-factory lora generated_from_trainer base_model:upstage/SOLAR-10.7B-v1.0 base_model:adapter:upstage/SOLAR-10.7B-v1.0 license:other region:us
---
base_model: upstage/SOLAR-10.7B-v1.0
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: solar-10b-ocn-v1
  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. -->

# solar-10b-ocn-v1

This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the oncc_medqa_instruct dataset.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Training script

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py --stage sft --do_train True --model_name_or_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning_type lora --quantization_bit 4 --flash_attn True --dataset_dir data --dataset oncc_medqa_instruct --cutoff_len 1024 --learning_rate 0.0005 --num_train_epochs 1.0 --max_samples 5000 --per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lr_scheduler_type cosine --max_grad_norm 1.0 --logging_steps 10 --save_steps 100 --warmup_steps 10 --neftune_noise_alpha 0.5 --lora_rank 8 --lora_dropout 0.2 --lora_target wqkv --output_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot_loss True


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

### Performance

Test script: 
lm_eval --model hf --model_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu_clinical_knowledge,mmlu_college_medicine,mmlu_professional_medicine --device cuda:0 --limit 100

hf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
|        Tasks        |Version|Filter|n-shot| Metric |Value|   |Stderr|
|---------------------|-------|------|-----:|--------|----:|---|-----:|
|pubmedqa             |      1|none  |     0|acc     | 0.95|±  |0.0219|
|medmcqa              |Yaml   |none  |     0|acc     | 0.42|±  |0.0496|
|professional_medicine|      0|none  |     0|acc     | 0.72|±  |0.0451|
|college_medicine     |      0|none  |     0|acc     | 0.67|±  |0.0473|
|clinical_knowledge   |      0|none  |     0|acc     | 0.64|±  |0.0482|
|ocn                  |Yaml   |none  |     0|acc     | 0.83|±  |0.0378|
|aocnp                |Yaml   |none  |     0|acc     | 0.72|±  |0.0451|


