README.md

---
license: openrail
---

# PMC_LLaMA

To obtain the foundation model in medical field, we propose [MedLLaMA_13B](https://huggingface.co./chaoyi-wu/MedLLaMA_13B) and PMC_LLaMA_13B.

MedLLaMA_13B is initialized from LLaMA-13B and further pretrained with medical corpus. Despite the expert knowledge gained, it lacks instruction-following ability.
Hereby we construct a instruction-tuning dataset and evaluate the tuned model.

As shown in the table, PMC_LLaMA_13B achieves comparable results to ChatGPT on medical QA benchmarks.

![medical_qa](https://pic4.zhimg.com/80/v2-bf43393cd753018e11fdb1c64a1a87df.png)


## Usage

```python
import transformers
import torch

tokenizer = transformers.LlamaTokenizer.from_pretrained('axiong/PMC_LLaMA_13B')
model = transformers.LlamaForCausalLM.from_pretrained('axiong/PMC_LLaMA_13B')

sentence = 'Hello, doctor' 
batch = tokenizer(
    sentence,
    return_tensors="pt", 
    add_special_tokens=False
)
with torch.no_grad():
    generated = model.generate(
        inputs = batch["input_ids"],
        max_length=200,
        do_sample=True,
        top_k=50
    )
    print('model predict: ',tokenizer.decode(generated[0]))
```