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from threading import Thread
from typing import Iterator
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
model_id = 'baichuan-inc/Baichuan2-13B-Chat'
if torch.cuda.is_available():
model = AutoModelForCausalLM.from_pretrained(
model_id,
# device_map='auto',
torch_dtype=torch.float16,
trust_remote_code=True
)
model = model.quantize(4).cuda()
model.generation_config = GenerationConfig.from_pretrained(model_id)
else:
model = None
tokenizer = AutoTokenizer.from_pretrained(
model_id,
use_fast=False,
trust_remote_code=True
)
def run(
message: str,
chat_history: list[tuple[str, str]],
max_new_tokens: int = 1024,
temperature: float = 1.0,
top_p: float = 0.95,
top_k: int = 5
) -> Iterator[str]:
model.generation_config.max_new_tokens = max_new_tokens
model.generation_config.temperature = temperature
model.generation_config.top_p = top_p
model.generation_config.top_k = top_k
history = []
result=""
for i in chat_history:
history.append({"role": "user", "content": i[0]})
history.append({"role": "assistant", "content": i[1]})
print(history)
history.append({"role": "user", "content": message})
for response in model.chat(
tokenizer,
history,
# stream=True,
):
result = result + response
yield result
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