Witllm/demo.py

66 lines
2.4 KiB
Python

import json
from chatglm import ChatGLMForConditionalGeneration
from chatglm import ChatGLMTokenizer
from transformers import AutoConfig
pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b"
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
return_unused_kwargs=True,
trust_remote_code=True,
code_revision=None,
_commit_hash=None,
)
glm = ChatGLMForConditionalGeneration(config)
tokenizer_config_file = "./chatglm/tokenizer_config.json"
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
init_kwargs.pop("tokenizer_class", None)
init_kwargs.pop("tokenizer_file", None)
saved_init_inputs = init_kwargs.pop("init_inputs", ())
init_inputs = saved_init_inputs
init_kwargs["vocab_file"] = "./chatglm/tokenizer.model"
init_kwargs["added_tokens_file"] = None
init_kwargs["special_tokens_map_file"] = None
init_kwargs["tokenizer_file"] = None
init_kwargs["name_or_path"] = pretrained_model_name_or_path
tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs)
glm = glm.from_pretrained(pretrained_model_name_or_path, config=config).half().cuda()
glm = glm.eval()
query = "colin"
response, history = glm.chat(tokenizer, query, history=[])
print(response)
query = "你好"
response, history = glm.chat(tokenizer, query, history=history)
print(response)
# response, history = glm.chat(tokenizer, "你是一个心理学专家,请问晚上睡不着应该怎么办", history=history)
# print(response)
# import plotly_express as px
# px.imshow(ron)
# gapminder = px.data.gapminder()
# gapminder2007 = gapminder.query('year == 2007')
# px.scatter(gapminder2007, x='gdpPercap', y='lifeExp')
# from modelscope import AutoTokenizer, AutoModel, snapshot_download
# model_dir = snapshot_download("ZhipuAI/chatglm3-6b", cache_dir="./chatglm", revision="v1.0.0")
# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).half().cuda()
# tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
# model = model.eval()
# response, history = model.chat(tokenizer, "colin", history=[])
# print(response)
# response, history = model.chat(tokenizer, "你好", history=history)
# print(response)
# # response, history = model.chat(tokenizer, "你是一个心理学专家,请问晚上睡不着应该怎么办", history=history)
# # print(response)