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model_name = "LLaMA"
cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
from threading import Thread
# ------------------------------------------------------------------------------------------------------------------------
# ππ» Local Model
# ------------------------------------------------------------------------------------------------------------------------
@SingletonLocalLLM
class GetONNXGLMHandle(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‘
import os, glob
import os
import platform
huggingface_token, device = get_conf('HUGGINGFACE_ACCESS_TOKEN', 'LOCAL_MODEL_DEVICE')
assert len(huggingface_token) != 0, "沑ζε‘«ε HUGGINGFACE_ACCESS_TOKEN"
with open(os.path.expanduser('~/.cache/huggingface/token'), 'w') as f:
f.write(huggingface_token)
model_id = 'meta-llama/Llama-2-7b-chat-hf'
with ProxyNetworkActivate('Download_LLM'):
self._tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=huggingface_token)
# use fp16
model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=huggingface_token).eval()
if device.startswith('cuda'): model = model.half().to(device)
self._model = model
return self._model, self._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']
console_slience = kwargs.get('console_slience', True)
return query, max_length, top_p, temperature, history, console_slience
def convert_messages_to_prompt(query, history):
prompt = ""
for a, b in history:
prompt += f"\n[INST]{a}[/INST]"
prompt += "\n{b}" + b
prompt += f"\n[INST]{query}[/INST]"
return prompt
query, max_length, top_p, temperature, history, console_slience = adaptor(kwargs)
prompt = convert_messages_to_prompt(query, history)
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
# code from transformers.llama
streamer = TextIteratorStreamer(self._tokenizer)
# Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
inputs = self._tokenizer([prompt], return_tensors="pt")
prompt_tk_back = self._tokenizer.batch_decode(inputs['input_ids'])[0]
generation_kwargs = dict(inputs.to(self._model.device), streamer=streamer, max_new_tokens=max_length)
thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
thread.start()
generated_text = ""
for new_text in streamer:
generated_text += new_text
if not console_slience: print(new_text, end='')
yield generated_text.lstrip(prompt_tk_back).rstrip("</s>")
if not console_slience: print()
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
def try_to_import_special_deps(self, **kwargs):
# import something that will raise error if the user does not install requirement_*.txt
# πββοΈπββοΈπββοΈ δΈ»θΏη¨ζ§θ‘
import importlib
importlib.import_module('transformers')
# ------------------------------------------------------------------------------------------------------------------------
# ππ» GPT-Academic Interface
# ------------------------------------------------------------------------------------------------------------------------
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name) |