EQUES
/

MedLLama3-JP-v2

@@ -48,198 +48,43 @@ GPTモデルのパフォーマンスは[Kasai et al., 2023](https://arxiv.org/pd
 ![](img/figure.png)
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 ![](img/figure.png)
+## Usage
+```
+pip install transformers vllm
+```
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+model_name = "EQUES/MedLLama3-JP-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(
+    model=model_name,
+    tensor_parallel_size=1,
+)
+sampling_params = SamplingParams(
+    temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
+)
+message = [
+    {"role": "system", "content": <question>},
+    {"role": "user","content": <answer>},
+    {"role": "system", "content": <question>},
+]
+prompt = tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
+output = llm.generate(prompt, sampling_params)
+print(output[0].outputs[0].text)
+```
 ## Bias, Risks, and Limitations
+The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.