| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| client = InferenceClient( | |
| "HuggingFaceH4/zephyr-7b-alpha" | |
| ) | |
| def format_prompt(message, history): | |
| system = "<|system|>When asked a question, answer only the question. Do no elaborate, or add on. Just answer the question in one to two sentences. You sentences should be at the 5th or 6th grade level.</s>\n" | |
| prompt = "" | |
| for user_prompt, bot_response in history: | |
| prompt += f"<|user|>\n{user_prompt}</s>\n" | |
| prompt += f"<|assistant|>\n{bot_response}</s>\n" | |
| prompt += f"<|user|>\n{message}</s>\n" | |
| return prompt | |
| def generate( | |
| prompt, history, temperature=0.9, max_new_tokens=500, 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=[ | |
| 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=256, | |
| minimum=0, | |
| maximum=1048, | |
| 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", | |
| ) | |
| ] | |
| css = """ | |
| #mkd { | |
| height: 500px; | |
| overflow: auto; | |
| border: 1px solid #ccc; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as inf: | |
| gr.HTML("<h1><center>zephyr-7b-alpha<h1><center>") | |
| gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha'>zephyr-7b-alpha</a> model. 💬<h3><center>") | |
| gr.ChatInterface( | |
| generate, | |
| additional_inputs=additional_inputs, | |
| examples=[["Can squirrel swims?"], ["Write a poem about squirrel."]] | |
| ) | |
| inf.queue().launch() |