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llama-2-7b-chat-test / model.py

harsh-manvar

Update model.py

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	from threading import Thread
	from typing import Iterator

	#import torch
	from transformers.utils import logging
	from ctransformers import AutoModelForCausalLM
	from transformers import TextIteratorStreamer, AutoTokenizer

	logging.set_verbosity_info()
	logger = logging.get_logger("transformers")

	config = {"max_new_tokens": 256, "repetition_penalty": 1.1,
	"temperature": 0.1, "stream": True}
	model_id = "TheBloke/Llama-2-7B-Chat-GGML"
	device = "cpu"


	model = AutoModelForCausalLM.from_pretrained(model_id, model_type="llama", lib="avx2", hf=True)
	tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")

	def get_prompt(message: str, chat_history: list[tuple[str, str]],
	system_prompt: str) -> str:
	#logger.info("get_prompt chat_history=%s",chat_history)
	#logger.info("get_prompt system_prompt=%s",system_prompt)
	texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
	#logger.info("texts=%s",texts)
	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
	#logger.info("get_prompt message=%s",message)
	texts.append(f'{message} [/INST]')
	#logger.info("get_prompt final texts=%s",texts)
	return ''.join(texts)


	def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
	#logger.info("get_input_token_length=%s",message)
	prompt = get_prompt(message, chat_history, system_prompt)
	#logger.info("prompt=%s",prompt)
	input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids']
	#logger.info("input_ids=%s",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 = 0.8,
	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', add_special_tokens=False).to(device)

	streamer = TextIteratorStreamer(tokenizer,
	timeout=15.,
	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)