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model_name = "deepseek-coder-6.7b-instruct"
cmd_to_install = "ζͺη₯" # "`pip install -r request_llms/requirements_qwen.txt`"
import os
from toolbox import ProxyNetworkActivate
from toolbox import get_conf
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
from threading import Thread
def download_huggingface_model(model_name, max_retry, local_dir):
from huggingface_hub import snapshot_download
for i in range(1, max_retry):
try:
snapshot_download(repo_id=model_name, local_dir=local_dir, resume_download=True)
break
except Exception as e:
print(f'\n\nδΈθ½½ε€±θ΄₯οΌιθ―第{i}欑δΈ...\n\n')
return local_dir
# ------------------------------------------------------------------------------------------------------------------------
# ππ» Local Model
# ------------------------------------------------------------------------------------------------------------------------
class GetCoderLMHandle(LocalLLMHandle):
def load_model_info(self):
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
self.model_name = model_name
self.cmd_to_install = cmd_to_install
def load_model_and_tokenizer(self):
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
with ProxyNetworkActivate('Download_LLM'):
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
model_name = "deepseek-ai/deepseek-coder-6.7b-instruct"
# local_dir = f"~/.cache/{model_name}"
# if not os.path.exists(local_dir):
# tokenizer = download_huggingface_model(model_name, max_retry=128, local_dir=local_dir)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
self._streamer = TextIteratorStreamer(tokenizer)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
if get_conf('LOCAL_MODEL_DEVICE') != 'cpu':
model = model.cuda()
return model, tokenizer
def llm_stream_generator(self, **kwargs):
# πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘
def adaptor(kwargs):
query = kwargs['query']
max_length = kwargs['max_length']
top_p = kwargs['top_p']
temperature = kwargs['temperature']
history = kwargs['history']
return query, max_length, top_p, temperature, history
query, max_length, top_p, temperature, history = adaptor(kwargs)
history.append({ 'role': 'user', 'content': query})
messages = history
inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt").to(self._model.device)
generation_kwargs = dict(
inputs=inputs,
max_new_tokens=max_length,
do_sample=False,
top_p=top_p,
streamer = self._streamer,
top_k=50,
temperature=temperature,
num_return_sequences=1,
eos_token_id=32021,
)
thread = Thread(target=self._model.generate, kwargs=generation_kwargs, daemon=True)
thread.start()
generated_text = ""
for new_text in self._streamer:
generated_text += new_text
# print(generated_text)
yield generated_text
def try_to_import_special_deps(self, **kwargs): pass
# import something that will raise error if the user does not install requirement_*.txt
# πββοΈπββοΈπββοΈ δΈ»θΏη¨ζ§θ‘
# import importlib
# importlib.import_module('modelscope')
# ------------------------------------------------------------------------------------------------------------------------
# ππ» GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetCoderLMHandle, model_name, history_format='chatglm3') |