from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM from peft import PeftModel, PeftConfig import torch import gradio as gr import random from textwrap import wrap EXAMPLES = [ ["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"], ["What's the Everett interpretation of quantum mechanics?"], ["Give me a list of the top 10 dive sites you would recommend around the world."], ["Can you tell me more about deep-water soloing?"], ["Can you write a short tweet about the release of our latest AI model, Falcon LLM?"] ] device = "cuda" if torch.cuda.is_available() else "cpu" base_model_id = "tiiuae/falcon-7b-instruct" model_directory = "Tonic/GaiaMiniMed" tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left") model_config = AutoConfig.from_pretrained(base_model_id) peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config) peft_model = PeftModel.from_pretrained(peft_model, model_directory) def format_prompt(message, history, system_prompt): prompt = "" if system_prompt: prompt += f"System: {system_prompt}\n" for user_prompt, bot_response in history: prompt += f"User: {user_prompt}\n" prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: " prompt += f"""User: {message} Falcon:""" return prompt seed = 42 def generate( prompt, history, system_prompt="", 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) global seed generate_kwargs = dict( temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=1.0, stop_sequences="[END]", do_sample=True, seed=seed, ) seed = seed + 1 formatted_prompt = format_prompt(prompt, history, system_prompt) try: 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 for stop_str in STOP_SEQUENCES: if output.endswith(stop_str): output = output[:-len(stop_str)] output = output.rstrip() yield output yield output except Exception as e: raise gr.Error(f"Error while generating: {e}") return output additional_inputs=[ gr.Textbox("", label="Optional system prompt"), 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=3000, step=64, interactive=True, info="The maximum numbers of new tokens", ), gr.Slider( label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, 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", ) ] with gr.Blocks() as demo: with gr.Row(): with gr.Column(scale=0.4): gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False) with gr.Column(): gr.Markdown( # 👋🏻Welcome to Tonic's GaiaMiniMed Chat🚀" "You can use this Space to test out the current model [(Tonic/GaiaMiniMed)](https://huggingface.co/Tonic/GaiaMiniMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)." ) gr.ChatInterface( generate, examples=EXAMPLES, additional_inputs=additional_inputs, ) demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)