Falcon-H1 is a new SoTA hybrid model by TII in Abu Dhabi. It is open source and available on Hugging Face. This demo is powered by OpenInnovationAI. Try out our chat interface.
""" def stream_chat( message: str, history: list, model_name: str, temperature: float = 0.1, max_new_tokens: int = 1024, top_p: float = 1.0, top_k: int = 20, presence_penalty: float = 1.2, ): """Chat function that streams responses from the selected model""" cfg = MODEL_CONFIGS[model_name] api_key = os.getenv(cfg["api_key_env"]) base_url = os.getenv(cfg.get("base_url_env", ""), None) if not api_key: yield f"❌ Env-var `{cfg['api_key_env']}` not set." return if cfg.get("base_url_env") and not base_url: yield f"❌ Env-var `{cfg['base_url_env']}` not set." return client = openai.OpenAI(api_key=api_key, base_url=base_url) msgs = [{"role": "system", "content": SYSTEM_PROMPT},] for u, a in history: msgs += [{"role": "user", "content": u}, {"role": "assistant", "content": a}] msgs.append({"role": "user", "content": message}) try: stream = client.chat.completions.create( model=cfg["model_id"], messages=msgs, temperature=temperature, top_p=top_p, max_tokens=max_new_tokens, presence_penalty=presence_penalty, stream=True, ) partial = "" for chunk in stream: if (delta := chunk.choices[0].delta).content: partial += delta.content yield partial except Exception as e: yield f"❌ Error: {str(e)}" # Create the Gradio interface with gr.Blocks(css=CSS, theme="soft") as demo: # Header section gr.HTML(TITLE) gr.HTML(SUB_TITLE.format(today_date=today.strftime('%B %d, %Y'))) gr.DuplicateButton(value="Duplicate Space", elem_classes="duplicate-button") # Create model selection with descriptions model_options = list(MODEL_CONFIGS.keys()) with gr.Row(): model_dropdown = gr.Dropdown( choices=model_options, value=model_options[0], label="Select Falcon-H1 Model", info="Choose which model checkpoint to use" ) # Create chatbot chatbot = gr.Chatbot(height=600, latex_delimiters=[ {"left": "$$", "right": "$$", "display": True}, # For display mode math {"left": "$", "right": "$", "display": False}, # For inline math {"left": "\\(", "right": "\\)", "display": False}, # Common inline delimiters {"left": "\\[", "right": "\\]", "display": True} # Common display delimiters ] ) # Message input area with a cleaner layout with gr.Row(): with gr.Column(scale=0.85): msg = gr.Textbox( scale=1, show_label=False, placeholder="Enter text and press enter", container=False ) with gr.Column(scale=0.15, min_width=0): submit_btn = gr.Button("Submit", variant="primary") # Parameters in accordion similar to second code with gr.Accordion("⚙️ Parameters", open=False, elem_classes="accordion"): temperature = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.05, label="Temperature", info="Higher values produce more diverse outputs") max_new_tokens = gr.Slider(minimum=64, maximum=4096*8, value=1024, step=64, label="Max new tokens", info="Maximum length of generated response") top_p = gr.Slider(minimum=0, maximum=1, value=1.0, step=0.05, label="top_p", info="1.0 means no filtering") top_k = gr.Slider(minimum=1, maximum=20, step=1, value=20, label="top_k") presence_penalty = gr.Slider(minimum=0, maximum=2, value=1.2, step=0.1, label="Presence penalty", info="Penalizes repetition") # Examples section gr.Examples( examples=[ ["Hello there, can you suggest a few places to visit in UAE?"], ["What is UAE known for?"], ], inputs=msg, ) # Chat handler function def user(user_message, history): return "", history + [[user_message, None]] def bot(history, model_choice, temp, max_tokens, top_p_val, top_k_val, penalty): user_message = history[-1][0] history[-1][1] = "" for character in stream_chat( user_message, history[:-1], model_choice, temp, max_tokens, top_p_val, top_k_val, penalty ): history[-1][1] = character yield history # Set up event handlers msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( bot, [chatbot, model_dropdown, temperature, max_new_tokens, top_p, top_k, presence_penalty], chatbot ) submit_btn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then( bot, [chatbot, model_dropdown, temperature, max_new_tokens, top_p, top_k, presence_penalty], chatbot ) if __name__ == "__main__": demo.launch()