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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co./docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| import requests | |
| from openai import OpenAI, AsyncOpenAI | |
| clients = { | |
| '32B (work in progress)': [OpenAI(api_key='123', base_url=os.getenv('MODEL_NAME_OR_PATH_32B')), os.getenv('MODEL_NAME_32B')], | |
| '32B QWQ (experimental, without any additional tuning after LEP!)': [OpenAI(api_key='123', base_url=os.getenv('MODEL_NAME_OR_PATH_QWQ')), os.getenv('MODEL_NAME_QWQ')], | |
| '7B (work in progress)': [OpenAI(api_key='123', base_url=os.getenv('MODEL_NAME_OR_PATH_7B')), 'RefalMachine/ruadapt_qwen2.5_7B_ext_u48_instruct'], | |
| '3B': [OpenAI(api_key='123', base_url=os.getenv('MODEL_NAME_OR_PATH_3B')), 'RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4'] | |
| } | |
| #client = InferenceClient(os.getenv('MODEL_NAME_OR_PATH')) | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| model_name, | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| repetition_penalty | |
| ): | |
| messages = [] | |
| if len(system_message.strip()) > 0: | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| res = clients[model_name][0].chat.completions.create( | |
| model=clients[model_name][1], | |
| messages=messages, | |
| temperature=temperature, | |
| top_p=top_p, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| extra_body={ | |
| "repetition_penalty": repetition_penalty, | |
| "add_generation_prompt": True, | |
| } | |
| ) | |
| for message in res: | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| options = ["32B (work in progress)", "32B QWQ (experimental, without any additional tuning after LEP!)", "7B (work in progress)", "3B"] | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Radio(choices=options, label="Model:", value=options[1]), | |
| gr.Textbox(value="You are a helpful and harmless assistant. You should think step-by-step. First, reason (the user does not see your reasoning), then give your final answer.", label="System message"), | |
| gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.0, maximum=2.0, value=0.3, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| gr.Slider(minimum=0.9, maximum=1.5, value=1.05, step=0.05, label="repetition_penalty"), | |
| ], | |
| concurrency_limit=10 | |
| ) | |
| if __name__ == "__main__": | |
| #print(requests.get(os.getenv('MODEL_NAME_OR_PATH')[:-3] + '/docs')) | |
| demo.launch(share=True) | |