Update README.md

README.md

@@ -1,3 +1,51 @@
----
-license: gemma
----
+---
+license: gemma
+---
+## Introduction
+This repo contains Gemma-2-9b-Medical, a medical language model with 9 billion parameters. This model builds upon the foundation of Gemma-2-9b-base and has been tuned with diverse medical and general instructions. We also use the three strategies in the paper 'Efficient Continual Pre-training by Mitigating the Stability Gap' to mitigate the stability gap during instruction tuning, which boosts the model's medical task performance and reduces the computation consumption.
+
+## 💻 Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+model_name = "YiDuo1999/Gemma-2-9b-medical"
+device_map = 'auto'
+model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True,use_cache=False,device_map=device_map)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+def askme(question):
+    sys_message = ''' 
+    You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
+    provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
+    '''   
+    # Create messages structured for the chat template
+    messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}]
+    
+    # Applying chat template
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=100, use_cache=True)
+    
+    # Extract and return the generated text, removing the prompt
+    response_text = tokenizer.batch_decode(outputs)[0].strip()
+    answer = response_text.split('<|im_start|>assistant')[-1].strip()
+    return answer
+## 🏆 Evaluation
+For question-answering tasks, we have
+
+| Model                          | MMLU-Medical | PubMedQA | MedMCQA | MedQA-4-Option | Avg  |
+|:--------------------------------|:--------------|:----------|:---------|:----------------|:------|
+| Mistral-7B-instruct   | 55.8         | 17.8     | 40.2    | 41.1           | 37.5 |
+| Zephyr-7B-instruct-β  | 63.3         | 46.0     | 43.0    | 48.5           | 48.7 |
+| PMC-Llama-7B        | 59.7         | 59.2     | 57.6    | 49.2           | 53.6 |
+| Medalpaca-13B     | 55.2         | 50.4     | 21.2    | 20.2           | 36.7 |
+| AlpaCare-13B    | 60.2         | 53.8     | 38.5    | 30.4           | 45.7 |
+| BioMedGPT-LM 7B  | 52.0         | 58.6     | 34.9    | 39.3           | 46.2 |
+| Me-Llama-13B      | -            | 70.0     | 44.9       | 42.7              | -    |
+| Llama-3-8B instruct                  | 82.0         | 74.6     | 57.1    | 60.3           | 68.5 |
+| JSL-Med-Sft-Llama-3-8B  | 83.0     | 75.4     | 57.5    | 74.8           | 72.7 |
+| GPT-3.5-turbo-1106                   | 74.0         | 72.6     | 34.9    | 39.3           | 60.6 |
+| GPT-4                  | 85.5         | 69.2     | 69.5    | 83.9           | 77.0 |
+| Gemma-2-9b-int                  | 75.0         | 76.0     | 40.3    | 48.9           | 60.q |
+| Llama-3-physician-8B instruct | 80.0         | 76.0     | 80.2    | 60.3           | 74.1 |