import os from typing import Iterator import gradio as gr from text_generation import Client HF_TOKEN = os.environ.get('HF_READ_TOKEN', False) EOS_STRING = '' EOT_STRING = '' def get_prompt(message, chat_history, system_prompt): texts = [f'[INST] <>\n{system_prompt}\n<>\n\n'] do_strip = False for user_input, response in chat_history: user_input = user_input.strip() if do_strip else user_input do_strip = True texts.append(f"{user_input} [/INST\ {response.strip()} [INST] ") message = message.strip() if do_strip else message texts.append(f"{message} [/INST]") return ''.join(texts) def run(model_id, message, chat_history, system_prompt, max_new_tokens=1024, temperature=0.3, top_p=0.9, top_k=50): API_URL = "https://api-inference.huggingface.co/models/" + model_id client = Client(API_URL, headers={'Authorization': f"Bearer {HF_TOKEN}"}) prompt = get_prompt(message, chat_history, system_prompt) generate_kwargs = dict( max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, top_k=top_k, temperature=temperature ) stream = client.generate_stream(prompt, **generate_kwargs) output = '' for response in stream: if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]): return output else: output += response.token.text yield output return output DEFAULT_SYSTEM_PROMPT = """ You are Jarvis. You are an AI assistant, you are moderately-polite and give only true information. You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. If you think there might not be a correct answer, you say so. Since you are autoregressive, each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, assumptions, and step-by-step thinking BEFORE you try to answer a question. """ MAX_MAX_NEW_TOKENS = 10240 DEFAULT_MAX_NEW_TOKENS = 4096 MAX_INPUT_TOKEN_LENGTH = 4000 DESCRIPTION = "#

He's just Jarvis. ;)

" def clear_and_save_textbox(message): return '', message def display_input(message, history=[]): history.append((message, '')) return history def delete_prev_fn(history=[]): try: message, _ = history.pop() except IndexError: message = '' return history, message or '' def generate(model_id, message, history_with_input, system_prompt, max_new_tokens, temperature, top_p, top_k): if max_new_tokens > MAX_MAX_NEW_TOKENS: raise ValueError history = history_with_input[:-1] generator = run(model_id, message, history, system_prompt, max_new_tokens, temperature, top_p, top_k) try: first_response = next(generator) yield history + [(message, first_response)] except StopIteration: yield history + [(message, '')] for response in generator: yield history + [(message, response)] def process_example(model_id, message): generator = generate(model_id, message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50) for x in generator: pass return '', x def check_input_token_length(message, chat_history, system_prompt): input_token_length = len(message) + len(chat_history) if input_token_length > MAX_INPUT_TOKEN_LENGTH: raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Client your chat history and try again.") with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo: gr.Markdown(DESCRIPTION) with gr.Group(): chatbot = gr.Chatbot(label='Jarvis') with gr.Row(): textbox = gr.Textbox(container=False, show_label=False, placeholder='Hey, Jarvis', scale=7) model_id = gr.Dropdown(label='LLM', choices=[ 'mistralai/Mistral-7B-Instruct-v0.1', 'HuggingFaceH4/zephyr-7b-beta', 'meta-llama/Llama-2-7b-chat-hf' ], value='mistralai/Mistral-7B-Instruct-v0.1', scale=3) submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0) with gr.Row(): retry_button = gr.Button('Retry', variant='secondary') undo_button = gr.Button('Undo', variant='secondary') clear_button = gr.Button('Clear', variant='secondary') saved_input = gr.State() with gr.Accordion(label='Advanced Options', open=False): system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=False) max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS) temperature = gr.Slider(label='Temperatur', minimum=0.1, maximum=4.0, step=0.1, value=0.1) top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9) top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=10) textbox.submit( fn=clear_and_save_textbox, inputs=textbox, outputs=[textbox, saved_input], api_name=False, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=False, queue=False, ).then( fn=check_input_token_length, inputs=[saved_input, chatbot, system_prompt], api_name=False, queue=False, ).success( fn=generate, inputs=[ model_id, saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=False, ) button_event_preprocess = submit_button.click( fn=clear_and_save_textbox, inputs=textbox, outputs=[textbox, saved_input], api_name=False, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=False, queue=False, ).then( fn=check_input_token_length, inputs=[saved_input, chatbot, system_prompt], api_name=False, queue=False, ).success( fn=generate, inputs=[ model_id, saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=False, ) retry_button.click( fn=delete_prev_fn, inputs=chatbot, outputs=[chatbot, saved_input], api_name=False, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=False, queue=False, ).then( fn=generate, inputs=[ model_id, saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=False, ) undo_button.click( fn=delete_prev_fn, inputs=chatbot, outputs=[chatbot, saved_input], api_name=False, queue=False, ).then( fn=lambda x: x, inputs=[saved_input], outputs=textbox, api_name=False, queue=False, ) clear_button.click( fn=lambda: ([], ''), outputs=[chatbot, saved_input], queue=False, api_name=False, ) demo.queue(max_size=32).launch(show_api=False)