Spaces:
Running on Zero
Running on Zero
| import spaces | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "O1-OPEN/OpenO1-LLama-8B-v0.1" | |
| 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=8192 | |
| ) | |
| 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() | |