from huggingface_hub import InferenceClient import gradio as gr import os API_URL = { "Mistral" : "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3", "Mixtral" : "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1", "Mathstral" : "https://api-inference.huggingface.co/models/mistralai/mathstral-7B-v0.1", } HF_TOKEN = os.environ['HF_TOKEN'] mistralClient = InferenceClient( API_URL["Mistral"], headers = {"Authorization" : f"Bearer {HF_TOKEN}"}, ) mixtralClient = InferenceClient( model = API_URL["Mixtral"], headers = {"Authorization" : f"Bearer {HF_TOKEN}"}, ) mathstralClient = InferenceClient( model = API_URL["Mathstral"], headers = {"Authorization" : f"Bearer {HF_TOKEN}"}, ) 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, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, model = "Mathstral"): # Selecting model to be used if(model == "Mistral"): client = mistralClient elif(model == "Mixstral"): client = mixtralClient elif(model == "Mathstral"): client = mixtralClient temperature = float(temperature) # Generation arguments 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) 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.Slider( label="Temperature", value=0.9, 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=2048, minimum=0, maximum=4096, step=64, interactive=True, info="The maximum numbers of new tokens", ), gr.Slider( label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens", ), gr.Slider( label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens", ), gr.Dropdown( choices = ["Mistral","Mixtral", "Mathstral"], value = "Mathstral", label = "Le modèle à utiliser", interactive=True, info = "Mistral : pour des conversations génériques, "+ "Mixtral : conversations plus rapides et plus performantes, "+ "Mathstral : raisonnement mathématiques et scientifique" ), ] css = """ #mkd { height: 500px; overflow: auto; border: 1px solid #ccc; } """ with gr.Blocks(css=css) as demo: gr.HTML("

Mathstral Test

") gr.HTML("

Dans cette démo, vous pouvez poser des questions mathématiques et scientifiques à Mathstral. 🧮

") gr.ChatInterface( generate, additional_inputs=additional_inputs, theme = gr.themes.Soft(), cache_examples=False, examples=[ [l.strip()] for l in open("exercices.md").readlines()], chatbot = gr.Chatbot( latex_delimiters=[ {"left" : "$$", "right": "$$", "display": True }, {"left" : "\\[", "right": "\\]", "display": True }, {"left" : "\\(", "right": "\\)", "display": False }, {"left": "$", "right": "$", "display": False } ] ) ) demo.queue(max_size=100).launch(debug=True)