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| import os | |
| import gradio as gr | |
| import clueai | |
| import torch | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| tokenizer = T5Tokenizer.from_pretrained("ClueAI/ChatYuan-large-v2") | |
| model = T5ForConditionalGeneration.from_pretrained("ClueAI/ChatYuan-large-v2") | |
| # 使用 | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model.to(device) | |
| model.half() | |
| base_info = "" | |
| def preprocess(text): | |
| text = f"{base_info}{text}" | |
| text = text.replace("\n", "\\n").replace("\t", "\\t") | |
| return text | |
| def postprocess(text): | |
| return text.replace("\\n", "\n").replace("\\t", "\t").replace( | |
| '%20', ' ') #.replace(" ", " ") | |
| generate_config = { | |
| 'do_sample': True, | |
| 'top_p': 0.9, | |
| 'top_k': 50, | |
| 'temperature': 0.7, | |
| 'num_beams': 1, | |
| 'max_length': 1024, | |
| 'min_length': 3, | |
| 'no_repeat_ngram_size': 5, | |
| 'length_penalty': 0.6, | |
| 'return_dict_in_generate': True, | |
| 'output_scores': True | |
| } | |
| def answer( | |
| text, | |
| top_p, | |
| temperature, | |
| sample=True, | |
| ): | |
| '''sample：是否抽样。生成任务，可以设置为True; | |
| top_p：0-1之间，生成的内容越多样''' | |
| text = preprocess(text) | |
| encoding = tokenizer(text=[text], | |
| truncation=True, | |
| padding=True, | |
| max_length=1024, | |
| return_tensors="pt").to(device) | |
| if not sample: | |
| out = model.generate(**encoding, | |
| return_dict_in_generate=True, | |
| output_scores=False, | |
| max_new_tokens=1024, | |
| num_beams=1, | |
| length_penalty=0.6) | |
| else: | |
| out = model.generate(**encoding, | |
| return_dict_in_generate=True, | |
| output_scores=False, | |
| max_new_tokens=1024, | |
| do_sample=True, | |
| top_p=top_p, | |
| temperature=temperature, | |
| no_repeat_ngram_size=12) | |
| #out=model.generate(**encoding, **generate_config) | |
| out_text = tokenizer.batch_decode(out["sequences"], | |
| skip_special_tokens=True) | |
| return postprocess(out_text[0]) | |
| def clear_session(): | |
| return '', None | |
| def chatyuan_bot(input, history, top_p, temperature, num): | |
| history = history or [] | |
| if len(history) > num: | |
| history = history[-num:] | |
| context = "\n".join([ | |
| f"用户：{input_text}\n小元：{answer_text}" | |
| for input_text, answer_text in history | |
| ]) | |
| #print(context) | |
| input_text = context + "\n用户：" + input + "\n小元：" | |
| input_text = input_text.strip() | |
| output_text = answer(input_text, top_p, temperature) | |
| print("open_model".center(20, "=")) | |
| print(f"{input_text}\n{output_text}") | |
| #print("="*20) | |
| history.append((input, output_text)) | |
| #print(history) | |
| return '', history, history | |
| def chatyuan_bot_regenerate(input, history, top_p, temperature, num): | |
| history = history or [] | |
| if history: | |
| input = history[-1][0] | |
| history = history[:-1] | |
| if len(history) > num: | |
| history = history[-num:] | |
| context = "\n".join([ | |
| f"用户：{input_text}\n小元：{answer_text}" | |
| for input_text, answer_text in history | |
| ]) | |
| #print(context) | |
| input_text = context + "\n用户：" + input + "\n小元：" | |
| input_text = input_text.strip() | |
| output_text = answer(input_text, top_p, temperature) | |
| print("open_model".center(20, "=")) | |
| print(f"{input_text}\n{output_text}") | |
| history.append((input, output_text)) | |
| #print(history) | |
| return '', history, history | |
| block = gr.Blocks() | |
| with block as demo: | |
| gr.Markdown("""<h1><center>元语智能——ChatYuan</center></h1> | |
| <font size=4>回答来自ChatYuan, 是模型生成的结果, 请谨慎辨别和参考, 不代表任何人观点 | Answer generated by ChatYuan model</font> | |
| <font size=4>注意：gradio对markdown代码格式展示有限</font> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| chatbot = gr.Chatbot(label='ChatYuan').style(height=400) | |
| with gr.Column(scale=1): | |
| num = gr.Slider(minimum=4, | |
| maximum=10, | |
| label="最大的对话轮数", | |
| value=5, | |
| step=1) | |
| top_p = gr.Slider(minimum=0, | |
| maximum=1, | |
| label="top_p", | |
| value=1, | |
| step=0.1) | |
| temperature = gr.Slider(minimum=0, | |
| maximum=1, | |
| label="temperature", | |
| value=0.7, | |
| step=0.1) | |
| clear_history = gr.Button("👋 清除历史对话 | Clear History") | |
| send = gr.Button("🚀 发送 | Send") | |
| regenerate = gr.Button("🚀 重新生成本次结果 | regenerate") | |
| message = gr.Textbox() | |
| state = gr.State() | |
| message.submit(chatyuan_bot, | |
| inputs=[message, state, top_p, temperature, num], | |
| outputs=[message, chatbot, state]) | |
| regenerate.click(chatyuan_bot_regenerate, | |
| inputs=[message, state, top_p, temperature, num], | |
| outputs=[message, chatbot, state]) | |
| send.click(chatyuan_bot, | |
| inputs=[message, state, top_p, temperature, num], | |
| outputs=[message, chatbot, state]) | |
| clear_history.click(fn=clear_session, | |
| inputs=[], | |
| outputs=[chatbot, state], | |
| queue=False) | |
| def ChatYuan(api_key, text_prompt, top_p): | |
| generate_config = { | |
| "do_sample": True, | |
| "top_p": top_p, | |
| "max_length": 128, | |
| "min_length": 10, | |
| "length_penalty": 1.0, | |
| "num_beams": 1 | |
| } | |
| cl = clueai.Client(api_key, check_api_key=True) | |
| # generate a prediction for a prompt | |
| # 需要返回得分的话，指定return_likelihoods="GENERATION" | |
| prediction = cl.generate(model_name='ChatYuan-large', prompt=text_prompt) | |
| # print the predicted text | |
| #print('prediction: {}'.format(prediction.generations[0].text)) | |
| response = prediction.generations[0].text | |
| if response == '': | |
| response = "很抱歉，我无法回答这个问题" | |
| return response | |
| def chatyuan_bot_api(api_key, input, history, top_p, num): | |
| history = history or [] | |
| if len(history) > num: | |
| history = history[-num:] | |
| context = "\n".join([ | |
| f"用户：{input_text}\n小元：{answer_text}" | |
| for input_text, answer_text in history | |
| ]) | |
| input_text = context + "\n用户：" + input + "\n小元：" | |
| input_text = input_text.strip() | |
| output_text = ChatYuan(api_key, input_text, top_p) | |
| print("api".center(20, "=")) | |
| print(f"api_key:{api_key}\n{input_text}\n{output_text}") | |
| history.append((input, output_text)) | |
| return '', history, history | |
| block = gr.Blocks() | |
| with block as demo_1: | |
| gr.Markdown("""<h1><center>元语智能——ChatYuan</center></h1> | |
| <font size=4>回答来自ChatYuan, 以上是模型生成的结果, 请谨慎辨别和参考, 不代表任何人观点 | Answer generated by ChatYuan model</font> | |
| <font size=4>注意：gradio对markdown代码格式展示有限</font> | |
| <font size=4>在使用此功能前，你需要有个API key. API key 可以通过这个<a href='https://www.clueai.cn/' target="_blank">平台</a>获取</font> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| chatbot = gr.Chatbot(label='ChatYuan').style(height=400) | |
| with gr.Column(scale=1): | |
| api_key = gr.inputs.Textbox(label="请输入你的api-key(必填)", | |
| default="", | |
| type='password') | |
| num = gr.Slider(minimum=4, | |
| maximum=10, | |
| label="最大的对话轮数", | |
| value=5, | |
| step=1) | |
| top_p = gr.Slider(minimum=0, | |
| maximum=1, | |
| label="top_p", | |
| value=1, | |
| step=0.1) | |
| clear_history = gr.Button("👋 清除历史对话 | Clear History") | |
| send = gr.Button("🚀 发送 | Send") | |
| message = gr.Textbox() | |
| state = gr.State() | |
| message.submit(chatyuan_bot_api, | |
| inputs=[api_key, message, state, top_p, num], | |
| outputs=[message, chatbot, state]) | |
| send.click(chatyuan_bot_api, | |
| inputs=[api_key, message, state, top_p, num], | |
| outputs=[message, chatbot, state]) | |
| clear_history.click(fn=clear_session, | |
| inputs=[], | |
| outputs=[chatbot, state], | |
| queue=False) | |
| block = gr.Blocks() | |
| with block as introduction: | |
| gr.Markdown("""<h1><center>元语智能——ChatYuan</center></h1> | |
| <font size=4>😉ChatYuan: 元语功能型对话大模型 | General Model for Dialogue with ChatYuan | |
| <br> | |
| 👏ChatYuan-large-v2是一个支持中英双语的功能型对话语言大模型，是继ChatYuan系列中ChatYuan-large-v1开源后的又一个开源模型。ChatYuan-large-v2使用了和 v1版本相同的技术方案，在微调数据、人类反馈强化学习、思维链等方面进行了优化。 | |
| <br> | |
| ChatYuan large v2 is an open-source large language model for dialogue, supports both Chinese and English languages, and in ChatGPT style. | |
| <br> | |
| ChatYuan-large-v2是ChatYuan系列中以轻量化实现高质量效果的模型之一，用户可以在消费级显卡、 PC甚至手机上进行推理（INT4 最低只需 400M ）。 | |
| <br> | |
| 在Chatyuan-large-v1的原有功能的基础上，我们给模型进行了如下优化： | |
| - 新增了中英双语对话能力。 | |
| - 新增了拒答能力。对于一些危险、有害的问题，学会了拒答处理。 | |
| - 新增了代码生成功能。对于基础代码生成进行了一定程度优化。 | |
| - 增强了基础能力。原有上下文问答、创意性写作能力明显提升。 | |
| - 新增了表格生成功能。使生成的表格内容和格式更适配。 | |
| - 增强了基础数学运算能力。 | |
| - 最大长度token数扩展到4096。 | |
| - 增强了模拟情景能力。.<br> | |
| <br> | |
| Based on the original functions of Chatyuan-large-v1, we optimized the model as follows: | |
| -Added the ability to speak in both Chinese and English. | |
| -Added the ability to refuse to answer. Learn to refuse to answer some dangerous and harmful questions. | |
| -Added code generation functionality. Basic code generation has been optimized to a certain extent. | |
| -Enhanced basic capabilities. The original contextual Q&A and creative writing skills have significantly improved. | |
| -Added a table generation function. Make the generated table content and format more appropriate. | |
| -Enhanced basic mathematical computing capabilities. | |
| -The maximum number of length tokens has been expanded to 4096. | |
| -Enhanced ability to simulate scenarios< br> | |
| <br> | |
| 👀<a href='https://www.cluebenchmarks.com/clueai.html'>PromptCLUE-large</a>在1000亿token中文语料上预训练, 累计学习1.5万亿中文token, 并且在数百种任务上进行Prompt任务式训练. 针对理解类任务, 如分类、情感分析、抽取等, 可以自定义标签体系; 针对多种生成任务, 可以进行采样自由生成. <br> | |
| <br> | |
| <a href='https://modelscope.cn/models/ClueAI/ChatYuan-large/summary' target="_blank">ModelScope</a> | <a href='https://huggingface.co/ClueAI/ChatYuan-large-v1' target="_blank">Huggingface</a> | <a href='https://www.clueai.cn' target="_blank">官网体验场</a> | <a href='https://github.com/clue-ai/clueai-python#ChatYuan%E5%8A%9F%E8%83%BD%E5%AF%B9%E8%AF%9D' target="_blank">ChatYuan-API</a> | <a href='https://github.com/clue-ai/ChatYuan' target="_blank">Github项目地址</a> | <a href='https://openi.pcl.ac.cn/ChatYuan/ChatYuan/src/branch/main/Fine_tuning_ChatYuan_large_with_pCLUE.ipynb' target="_blank">OpenI免费试用</a> | |
| </font> | |
| <center><a href="https://clustrmaps.com/site/1bts0" title="Visit tracker"><img src="//www.clustrmaps.com/map_v2.png?d=ycVCe17noTYFDs30w7AmkFaE-TwabMBukDP1802_Lts&cl=ffffff" /></a></center> | |
| """) | |
| gui = gr.TabbedInterface( | |
| interface_list=[introduction, demo, demo_1], | |
| tab_names=["相关介绍 | Introduction", "开源模型 | Online Demo", "API调用"]) | |
| gui.launch(quiet=True, show_api=False, share=False) | |