from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") def tokenize(text): return text # return tok.encode(text, add_special_tokens=False) 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 format_prompt(message, history): # prompt = "" # for user_prompt, bot_response in history: # prompt += "" + tokenize("[INST]") + tokenize(user_prompt) + tokenize("[/INST]") # prompt += tokenize(bot_response) + " " # prompt += tokenize("[INST]") + tokenize(message) + tokenize("[/INST]") # return prompt def generate(prompt, history, system_prompt, temperature=0.2, max_new_tokens=512, 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(prompt, history) 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=0.2, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs", ), gr.Slider( label="Max new tokens", value=512, minimum=0, maximum=1048, step=64, interactive=True, info="The maximum numbers of new tokens", ), gr.Slider( label="Top-p (nucleus sampling)", value=0.95, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens", ), gr.Slider( label="Repetition penalty", value=1, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens", ) ] # mychatbot = gr.Chatbot( # avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) # demo = gr.ChatInterface(fn=generate, # chatbot=mychatbot, # additional_inputs=additional_inputs, # title="Kamran's Mixtral 8x7b Chat", # retry_btn=None, # undo_btn=None # ) # demo.queue().launch(show_api=False) examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ], ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,], ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,], ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,], ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,], ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,], ] 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="Mixtral 46.7B", examples=examples, concurrency_limit=20, ).launch(show_api=False)