interface with qwen

This commit is contained in:
binary-husky 2023-08-07 01:24:41 +08:00
parent 9bee676cd2
commit 4d70b3786f
5 changed files with 77 additions and 3 deletions

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model_name = "ChatGLM-ONNX"
cmd_to_install = "`pip install request_llm/requirements_chatglm_onnx.txt`"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm_onnx.txt`"
from transformers import AutoModel, AutoTokenizer

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@ -1,5 +1,5 @@
model_name = "InternLM"
cmd_to_install = "`pip install request_llm/requirements_chatglm.txt`"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
from transformers import AutoModel, AutoTokenizer
import time

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model_name = "Qwen"
cmd_to_install = "`pip install -r request_llm/requirements_qwen.txt`"
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Local Model
# ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetONNXGLMHandle(LocalLLMHandle):
def load_model_info(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
self.model_name = model_name
self.cmd_to_install = cmd_to_install
def load_model_and_tokenizer(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
import os, glob
import os
import platform
from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_id = 'qwen/Qwen-7B-Chat'
revision = 'v1.0.1'
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, trust_remote_code=True)
# use fp16
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", revision=revision,
trust_remote_code=True, fp16=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_id,
trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
self._model = model
return self._model, None
def llm_stream_generator(self, **kwargs):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
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)
prompt = chat_template(history, query)
for response in model.chat(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
# 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
# from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
pass
# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name)

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modelscope
transformers_stream_generator

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@ -15,7 +15,8 @@ if __name__ == "__main__":
# from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
# from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
# from request_llm.bridge_claude import predict_no_ui_long_connection
from request_llm.bridge_internlm import predict_no_ui_long_connection
# from request_llm.bridge_internlm import predict_no_ui_long_connection
from request_llm.bridge_qwen import predict_no_ui_long_connection
llm_kwargs = {
'max_length': 512,