import os import gradio as gr from text_generation import Client, InferenceAPIClient def get_client(model: str): if model == "Rallio67/joi2_20B_instruct_alpha": return Client(os.getenv("JOI_API_URL")) if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B": return Client(os.getenv("OPENCHAT_API_URL")) return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None)) def get_usernames(model: str): if model == "Rallio67/joi2_20B_instruct_alpha": return "User: ", "Joi: " if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B": return ": ", ": " return "User: ", "Assistant: " def predict( model: str, inputs: str, top_p: float, temperature: float, top_k: int, repetition_penalty: float, watermark: bool, chatbot, history, ): client = get_client(model) user_name, assistant_name = get_usernames(model) history.append(inputs) past = [] for data in chatbot: user_data, model_data = data if not user_data.startswith(user_name): user_data = user_name + user_data if not model_data.startswith("\n\n" + assistant_name): model_data = "\n\n" + assistant_name + model_data past.append(user_data + model_data + "\n\n") if not inputs.startswith(user_name): inputs = user_name + inputs total_inputs = "".join(past) + inputs + "\n\n" + assistant_name partial_words = "" for i, response in enumerate(client.generate_stream( total_inputs, top_p=top_p if top_p < 1.0 else None, top_k=top_k, truncate=1000, repetition_penalty=repetition_penalty, watermark=watermark, temperature=temperature, max_new_tokens=500, stop_sequences=[user_name.rstrip(), assistant_name.rstrip()], )): if response.token.special: continue partial_words = partial_words + response.token.text if partial_words.endswith(user_name.rstrip()): partial_words = partial_words.rstrip(user_name.rstrip()) if partial_words.endswith(assistant_name.rstrip()): partial_words = partial_words.rstrip(assistant_name.rstrip()) if i == 0: history.append(" " + partial_words) else: history[-1] = partial_words chat = [ (history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2) ] yield chat, history def reset_textbox(): return gr.update(value="") title = """

🔥Large Language Model API 🚀Streaming🚀

""" description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form: ``` User: Assistant: User: Assistant: ... ``` In this app, you can explore the outputs of multiple LLMs when prompted in this way. """ with gr.Blocks( css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""" ) as demo: gr.HTML(title) with gr.Column(elem_id="col_container"): model = gr.Radio( value="Rallio67/joi2_20B_instruct_alpha", choices=[ "togethercomputer/GPT-NeoXT-Chat-Base-20B", "Rallio67/joi2_20B_instruct_alpha", "google/flan-t5-xxl", "google/flan-ul2", # "bigscience/bloom", "bigscience/bloomz", "EleutherAI/gpt-neox-20b", ], label="Model", interactive=True, ) chatbot = gr.Chatbot(elem_id="chatbot") inputs = gr.Textbox( placeholder="Hi there!", label="Type an input and press Enter" ) state = gr.State([]) b1 = gr.Button() with gr.Accordion("Parameters", open=False): top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)", ) temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.5, step=0.1, interactive=True, label="Temperature", ) top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k", ) repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", ) watermark = gr.Checkbox(value=True, label="Text watermarking") inputs.submit( predict, [ model, inputs, top_p, temperature, top_k, repetition_penalty, watermark, chatbot, state, ], [chatbot, state], ) b1.click( predict, [ model, inputs, top_p, temperature, top_k, repetition_penalty, watermark, chatbot, state, ], [chatbot, state], ) b1.click(reset_textbox, [], [inputs]) inputs.submit(reset_textbox, [], [inputs]) gr.Markdown(description) demo.queue(concurrency_count=16).launch(debug=True)