import json import os import shutil import requests import gradio as gr from huggingface_hub import Repository, InferenceClient HF_TOKEN = os.environ.get("HF_TOKEN", None) API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-180B-chat" BOT_NAME = "Falcon" STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"] 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?"] ] client = InferenceClient( API_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, ) 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 def generate( prompt, history, system_prompt="", 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, stop_sequences=STOP_SEQUENCES, do_sample=True, seed=42, ) formatted_prompt = format_prompt(prompt, history, system_prompt) 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 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=8192, 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", ) ] with gr.Blocks() as demo: with gr.Row(): with gr.Column(): gr.Image("home-banner.jpg", elem_id="banner-image", show_label=False) with gr.Column(): gr.Markdown( """**Chat with [Falcon-180B-Chat](https://huggingface.co/tiiuae/falcon-180b-chat), brainstorm ideas, discuss your holiday plans, and more!** ✨ This demo is powered by [Falcon-180B](https://huggingface.co/tiiuae/falcon-180B) and finetuned on a mixture of [Ultrachat](https://huggingface.co/datasets/stingning/ultrachat), [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) and [Airoboros](https://huggingface.co/datasets/jondurbin/airoboros-2.1). [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b) is a state-of-the-art large language model built by the [Technology Innovation Institute](https://www.tii.ae) in Abu Dhabi. It is trained on 3.5 trillion tokens (including [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)) and available under the Apache 2.0 license. It currently holds the 🥇 1st place on the [🤗 Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for a pretrained model. 🧪 This is only a **first experimental preview**: we intend to provide increasingly capable versions of Falcon Chat in the future, based on improved datasets and RLHF/RLAIF. 👀 **Learn more about Falcon LLM:** [falconllm.tii.ae](https://falconllm.tii.ae/) ➡️️ **Intended Use**: this demo is intended to showcase an early finetuning of [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b), to illustrate the impact (and limitations) of finetuning on a dataset of conversations and instructions. We encourage the community to further build upon the base model, and to create even better instruct/chat versions! ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words. """ ) gr.ChatInterface( generate, examples=EXAMPLES, additional_inputs=additional_inputs, ) demo.queue(concurrency_count=16).launch(debug=True)