File size: 3,010 Bytes
17d0a32 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
model_name = "Qwen"
cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
# ------------------------------------------------------------------------------------------------------------------------
# ๐๐ป Local Model
# ------------------------------------------------------------------------------------------------------------------------
class GetONNXGLMHandle(LocalLLMHandle):
def load_model_info(self):
# ๐โโ๏ธ๐โโ๏ธ๐โโ๏ธ ๅญ่ฟ็จๆง่ก
self.model_name = model_name
self.cmd_to_install = cmd_to_install
def load_model_and_tokenizer(self):
# ๐โโ๏ธ๐โโ๏ธ๐โโ๏ธ ๅญ่ฟ็จๆง่ก
import os, glob
import os
import platform
from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
with ProxyNetworkActivate('Download_LLM'):
model_id = 'qwen/Qwen-7B-Chat'
self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
# use fp16
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # ๅฏๆๅฎไธๅ็็ๆ้ฟๅบฆใtop_p็ญ็ธๅ
ณ่ถ
ๅ
self._model = model
return self._model, self._tokenizer
def llm_stream_generator(self, **kwargs):
# ๐โโ๏ธ๐โโ๏ธ๐โโ๏ธ ๅญ่ฟ็จๆง่ก
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)
for response in self._model.chat(self._tokenizer, query, history=history, stream=True):
yield response
def try_to_import_special_deps(self, **kwargs):
# import something that will raise error if the user does not install requirement_*.txt
# ๐โโ๏ธ๐โโ๏ธ๐โโ๏ธ ไธป่ฟ็จๆง่ก
import importlib
importlib.import_module('modelscope')
# ------------------------------------------------------------------------------------------------------------------------
# ๐๐ป GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name) |