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Running
on
Zero
Running
on
Zero
| from threading import Thread | |
| from typing import Iterator | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
| model_id = 'meta-llama/Llama-2-7b-chat-hf' | |
| if torch.cuda.is_available(): | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| device_map='auto' | |
| ) | |
| else: | |
| model = None | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| def get_prompt(message: str, chat_history: list[tuple[str, str]], | |
| system_prompt: str) -> str: | |
| texts = [f'[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n'] | |
| for user_input, response in chat_history: | |
| texts.append(f'{user_input} [/INST] {response} [INST] ') | |
| texts.append(f'{message.strip()} [/INST]') | |
| return ''.join(texts) | |
| def run(message: str, | |
| chat_history: list[tuple[str, str]], | |
| system_prompt: str, | |
| max_new_tokens: int = 1024, | |
| temperature: float = 0.8, | |
| top_p: float = 0.95, | |
| top_k: int = 50) -> Iterator[str]: | |
| prompt = get_prompt(message, chat_history, system_prompt) | |
| inputs = tokenizer([prompt], return_tensors='pt').to("cuda") | |
| streamer = TextIteratorStreamer(tokenizer, | |
| timeout=10., | |
| skip_prompt=True, | |
| skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| inputs, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=top_p, | |
| top_k=top_k, | |
| temperature=temperature, | |
| num_beams=1, | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| outputs = [] | |
| for text in streamer: | |
| outputs.append(text) | |
| yield ''.join(outputs) | |