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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)
base_info = "用户:你是谁?\n小元:我是元语智能公司研发的AI智能助手, 在不违反原则的情况下,我可以回答你的任何问题。\n"
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, sample=True, top_p=0.9, temperature=0.7):
'''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):
history = history or []
if len(history) > 5:
history = history[-5:]
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)
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):
history = history or []
if history:
input=history[-1][0]
history=history[:-1]
if len(history) > 5:
history = history[-5:]
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:"
output_text = answer(input_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>
""")
chatbot = gr.Chatbot(label='ChatYuan')
message = gr.Textbox()
state = gr.State()
message.submit(chatyuan_bot, inputs=[message, state], outputs=[chatbot, state])
with gr.Row():
clear_history = gr.Button("👋 清除历史对话 | Clear")
clear = gr.Button('🧹 清除发送框 | Clear Input')
send = gr.Button("🚀 发送 | Send")
regenerate = gr.Button("🚀 重新生成本次结果 | regenerate")
regenerate.click(chatyuan_bot_regenerate, inputs=[message, state], outputs=[chatbot, state])
send.click(chatyuan_bot, inputs=[message, state], outputs=[chatbot, state])
clear.click(lambda: None, None, message, queue=False)
clear_history.click(fn=clear_session , inputs=[], outputs=[chatbot, state], queue=False)
def ChatYuan(api_key, text_prompt):
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):
history = history or []
if len(history) > 5:
history = history[-5:]
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 = ChatYuan(api_key, input_text)
print("api".center(20, "="))
print(f"api_key:{api_key}\n{input_text}\n{output_text}")
#print("="*20)
history.append((input, output_text))
#print(history)
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>
""")
api_key = gr.inputs.Textbox(label="请输入你的api-key(必填)", default="", type='password')
chatbot = gr.Chatbot(label='ChatYuan')
message = gr.Textbox()
state = gr.State()
message.submit(chatyuan_bot_api, inputs=[api_key,message, state], outputs=[chatbot, state])
with gr.Row():
clear_history = gr.Button("👋 清除历史对话 | Clear Context")
clear = gr.Button('🧹 清除发送框 | Clear Input')
send = gr.Button("🚀 发送 | Send")
send.click(chatyuan_bot_api, inputs=[api_key,message, state], outputs=[chatbot, state])
clear.click(lambda: None, None, message, queue=False)
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>
&nbsp; <a href='https://modelscope.cn/models/ClueAI/ChatYuan-large/summary' target="_blank">ModelScope</a> &nbsp; | &nbsp; <a href='https://huggingface.co/ClueAI/ChatYuan-large-v1' target="_blank">Huggingface</a> &nbsp; | &nbsp; <a href='https://www.clueai.cn' target="_blank">官网体验场</a> &nbsp; | &nbsp; <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> &nbsp; | &nbsp; <a href='https://github.com/clue-ai/ChatYuan' target="_blank">Github项目地址</a> &nbsp; | &nbsp; <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> &nbsp;
</font>
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""")
gui = gr.TabbedInterface(interface_list=[introduction,demo, demo_1], tab_names=["相关介绍","开源模型", "API调用"])
gui.launch(quiet=True,show_api=False, share = False)