66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
import json
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from chatglm import ChatGLMForConditionalGeneration
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from chatglm import ChatGLMTokenizer
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from transformers import AutoConfig
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pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b"
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config, kwargs = AutoConfig.from_pretrained(
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pretrained_model_name_or_path,
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return_unused_kwargs=True,
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trust_remote_code=True,
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code_revision=None,
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_commit_hash=None,
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)
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glm = ChatGLMForConditionalGeneration(config)
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tokenizer_config_file = "./chatglm/tokenizer_config.json"
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if tokenizer_config_file is not None:
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with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
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init_kwargs = json.load(tokenizer_config_handle)
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init_kwargs.pop("tokenizer_class", None)
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init_kwargs.pop("tokenizer_file", None)
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saved_init_inputs = init_kwargs.pop("init_inputs", ())
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init_inputs = saved_init_inputs
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init_kwargs["vocab_file"] = "./chatglm/tokenizer.model"
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init_kwargs["added_tokens_file"] = None
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init_kwargs["special_tokens_map_file"] = None
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init_kwargs["tokenizer_file"] = None
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init_kwargs["name_or_path"] = pretrained_model_name_or_path
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tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs)
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glm = glm.from_pretrained(pretrained_model_name_or_path, config=config).half().cuda()
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glm = glm.eval()
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query = "colin"
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response, history = glm.chat(tokenizer, query, history=[])
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print(response)
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query = "你好"
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response, history = glm.chat(tokenizer, query, history=history)
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print(response)
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# 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)
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# print(response)
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# import plotly_express as px
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# px.imshow(ron)
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# gapminder = px.data.gapminder()
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# gapminder2007 = gapminder.query('year == 2007')
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# px.scatter(gapminder2007, x='gdpPercap', y='lifeExp')
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# from modelscope import AutoTokenizer, AutoModel, snapshot_download
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# model_dir = snapshot_download("ZhipuAI/chatglm3-6b", cache_dir="./chatglm", revision="v1.0.0")
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# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).half().cuda()
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# tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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# model = model.eval()
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# response, history = model.chat(tokenizer, "colin", history=[])
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# print(response)
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# response, history = model.chat(tokenizer, "你好", history=history)
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# print(response)
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# # 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)
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# # print(response)
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