from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient( "mistralai/Mixtral-8x7B-Instruct-v0.1" ) def format_prompt(message, history): prompt = "" for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " prompt += f"[INST] {message} [/INST]" return prompt def generate( prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, ): temperature = float(temperature) if temperature < 1e-2: temperature = 1e-2 top_p = float(top_p) generate_kwargs = dict( temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=repetition_penalty, do_sample=True, seed=42, ) formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history) stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: output += response.token.text yield output return output additional_inputs=[ gr.Textbox( label="System Prompt", max_lines=1, interactive=True, ), gr.Slider( label="Temperature", value=0.5, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Более высокое значение, даёт более разнообразные результаты.", ), gr.Slider( label="Max new tokens", value=16512, minimum=0, maximum=32768, step=64, interactive=True, info="Максимальное количество токенов", ), gr.Slider( label="Top-p (nucleus sampling)", value=0.75, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Более высокое значение, даёт большее разнообразие ", ), gr.Slider( label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Степень наказания за повторение токенов", ) ] examples=[["", "Отвечай всегда полностью на русском языке", 0.2, 16512, 0.90, 1.2], ] gr.ChatInterface( fn=generate, chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"), additional_inputs=additional_inputs, title="Mix-OpenAI-Chat", examples=examples, concurrency_limit=20, ).launch(show_api=False)