model card

README.md

@@ -1,3 +1,41 @@
 ---
 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]))
+```
+