from gradio import inputs, outputs, Interface from huggingface_hub import InferenceClient client = InferenceClient("mistralai/Mistral-7B-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, 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(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 = [ inputs.Slider( label="Temperature", default=0.9, min=0.0, max=1.0, step=0.05, description="Higher values produce more diverse outputs", ), inputs.Slider( label="Max new tokens", default=256, min=0, max=1048, step=64, description="The maximum numbers of new tokens", ), inputs.Slider( label="Top-p (nucleus sampling)", default=0.90, min=0.0, max=1, step=0.05, description="Higher values sample more low-probability tokens", ), inputs.Slider( label="Repetition penalty", default=1.2, min=1.0, max=2.0, step=0.05, description="Penalize repeated tokens", ), ] interface = Interface( fn=generate, inputs=[ inputs.Textbox( label="User Prompt", lines=2, placeholder="Type your message here...", ), inputs.Textbox( label="Bot Response", lines=2, placeholder="Bot's response will appear here...", ), *additional_inputs, ], outputs=outputs.Textbox(label="Conversation", lines=10), title="Mistral 7B", layout="vertical", theme="compact", ) interface.launch(share=False)