from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3") 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=1.0, max_new_tokens=1048, top_p=1.0, 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) 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=1.0, minimum=0.0, maximum=1.0, step=0.01, interactive=True, info="Higher values produce more diverse outputs", ), gr.Slider( label="Max new tokens", value=1048, minimum=0, maximum=2048, step=128, interactive=True, info="The maximum numbers of new tokens", ), gr.Slider( label="Top-p (nucleus sampling)", value=1.0, minimum=0.0, maximum=1.0, step=0.01, interactive=True, info="Higher values sample more low-probability tokens", ), gr.Slider( label="Repetition penalty", value=1.0, minimum=1.0, maximum=2.0, step=0.01, interactive=True, info="Penalize repeated tokens", ) ] 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="Mistral 7B v0.3" ).launch(show_api=False) gr.load("models/ehristoforu/dalle-3-xl-v2").launch() gr.load("models/microsoft/Phi-3-mini-4k-instruct").launch()