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() response, history = glm.chat(tokenizer, "colin", history=[]) print(response) response, history = glm.chat(tokenizer, "你好", 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)