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("""

元语智能——ChatYuan

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 注意：gradio对markdown代码格式展示有限 """) 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("""

元语智能——ChatYuan

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 注意：gradio对markdown代码格式展示有限 在使用此功能前，你需要有个API key. API key 可以通过这个平台获取 """) 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("""

元语智能——ChatYuan

😉ChatYuan: 元语功能型对话大模型 | General Model for Dialogue with ChatYuan
👏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。
ChatYuan large v2 is an open-source large language model for dialogue, supports both Chinese and English languages, and in ChatGPT style.
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 ）。
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。.

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>
👀PromptCLUE-large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.

  ModelScope   |   Huggingface   |   官网体验场   |   ChatYuan-API   |   Github项目地址   |   OpenI免费试用  
""") 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)