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Runtime error
| from threading import Thread | |
| from typing import Iterator | |
| import torch | |
| from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
| model_id = 'codellama/CodeLlama-13b-Instruct-hf' | |
| if torch.cuda.is_available(): | |
| config = AutoConfig.from_pretrained(model_id) | |
| config.pretraining_tp = 1 | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| config=config, | |
| torch_dtype=torch.float16, | |
| load_in_4bit=True, | |
| device_map='auto', | |
| use_safetensors=False, | |
| ) | |
| 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'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n'] | |
| # The first user input is _not_ stripped | |
| do_strip = False | |
| for user_input, response in chat_history: | |
| user_input = user_input.strip() if do_strip else user_input | |
| do_strip = True | |
| texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ') | |
| message = message.strip() if do_strip else message | |
| texts.append(f'{message} [/INST]') | |
| return ''.join(texts) | |
| def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int: | |
| prompt = get_prompt(message, chat_history, system_prompt) | |
| input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids'] | |
| return input_ids.shape[-1] | |
| def run(message: str, | |
| chat_history: list[tuple[str, str]], | |
| system_prompt: str, | |
| max_new_tokens: int = 1024, | |
| temperature: float = 0.1, | |
| top_p: float = 0.9, | |
| top_k: int = 50) -> Iterator[str]: | |
| prompt = get_prompt(message, chat_history, system_prompt) | |
| inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).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) | |