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from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "happzy2633/qwen2.5-7b-ins-v3"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def api_call(messages):
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
def call_gpt(history, prompt):
return api_call(history+[{"role":"user", "content":prompt}])
if __name__ == "__main__":
messages = [{"role":"user", "content":"你是谁？"}]
print(api_call(messages))
breakpoint()
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