peft safetensors llama-factory lora generated_from_trainer base_model:CausalLM/34b-beta base_model:adapter:CausalLM/34b-beta license:other region:us
---
base_model: CausalLM/34b-beta
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: model-update
  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. -->

# model-update

This model is a fine-tuned version of [CausalLM/34b-beta](https://huggingface.co/CausalLM/34b-beta) on the oncc_medqa_instruct dataset.

## Training procedure

```
git clone https://github.com/chenhaodev/LLaMA-Factory; cd LLaMA-Factory; pip install -r requirements.txt;
python create_pods.py 'CausalLM/34b-beta' 'NVIDIA A100 80GB PCIe' 1  xxx xxx xxx "--per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lora_target q_proj,v_proj --template llama2 --dataset oncc_medqa_instruct" 902i850eaw
```

### 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: 20
- num_epochs: 1.0

### Training results



### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.0.1+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2