import os import requests import json import gradio as gr from transformers import AutoTokenizer DESCRIPTION = """ # Demo: Breeze-7B-Instruct-v0.1 Breeze-7B is a language model family that builds on top of [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use. [Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0.1) is the base model for the Breeze-7B series. It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case. [Breeze-7B-Instruct](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0.1) derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks. [Breeze-7B-Instruct-64k](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0.1) is a slightly modified version of Breeze-7B-Instruct to enable a 64k-token context length. Roughly speaking, that is equivalent to 88k Traditional Chinese characters. The current release version of Breeze-7B is v0.1. *A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.* **免責聲明: Breeze-7B-Instruct 和 Breeze-7B-Instruct-64k 並未針對問答進行安全保護，因此語言模型的任何回應不代表 MediaTek Research 立場。** """ LICENSE = """ """ DEFAULT_SYSTEM_PROMPT = "You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan." API_URL = os.environ.get("API_URL") TOKEN = os.environ.get("TOKEN") HEADERS = { "accept": "application/json", "Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json", } MODEL_NAME="breeze-7b-instruct-v01" PRESENCE_PENALTY=0 FREQUENCY_PENALTY=0 model_name = "MediaTek-Research/Breeze-7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_name) with gr.Blocks() as demo: gr.Markdown(DESCRIPTION) chatbot = gr.Chatbot() with gr.Row(): msg = gr.Textbox( container=False, show_label=False, placeholder='Type a message...', scale=10, ) 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 = 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=6) max_new_tokens = gr.Slider( label='Max new tokens', minimum=32, maximum=1024, step=1, value=512, ) temperature = gr.Slider( label='Temperature', minimum=0.01, maximum=0.5, step=0.01, value=0.01, ) top_p = gr.Slider( label='Top-p (nucleus sampling)', minimum=0.01, maximum=1.0, step=0.01, value=0.01, ) def user(user_message, history): return "", history + [[user_message, None]] def bot(history, max_new_tokens, temperature, top_p, system_prompt): chat_data = [] system_prompt = system_prompt.strip() if system_prompt: chat_data.append({"role": "system", "content": system_prompt}) for user_msg, assistant_msg in history: if user_msg is not None: chat_data.append({"role": "user", "content": user_msg}) if assistant_msg is not None: chat_data.append({"role": "assistant", "content": assistant_msg}) message = tokenizer.apply_chat_template(chat_data, tokenize=False) message = message[3:] # remove SOT token data = { "model": MODEL_NAME, "prompt": str(message), "temperature": float(temperature) + 0.01, "n": 1, "max_tokens": int(max_new_tokens), "stop": "", "top_p": float(top_p), "logprobs": 0, "echo": False, "presence_penalty": PRESENCE_PENALTY, "frequency_penalty": FREQUENCY_PENALTY, "stream": True, } with requests.post(API_URL, headers=HEADERS, data=json.dumps(data), stream=True) as r: for response in r.iter_lines(): if len(response) > 0: text = response.decode() if text != "data: [DONE]": if text.startswith("data: "): text = text[5:] delta = json.loads(text)["choices"][0]["text"] if history[-1][1] is None: history[-1][1] = delta else: history[-1][1] += delta yield history if history[-1][1].endswith(''): history[-1][1] = history[-1][1][:-4] yield history print('== Record ==\nQuery: {query}\nResponse: {response}'.format(query=repr(message), response=repr(history[-1][1]))) msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( fn=bot, inputs=[ chatbot, max_new_tokens, temperature, top_p, system_prompt, ], outputs=chatbot ) submit_button.click( user, [msg, chatbot], [msg, chatbot], queue=False ).then( fn=bot, inputs=[ chatbot, max_new_tokens, temperature, top_p, system_prompt, ], outputs=chatbot ) def delete_prev_fn( history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]: try: message, _ = history.pop() except IndexError: message = '' return history, message or '' def display_input(message: str, history: list[tuple[str, str]]) -> list[tuple[str, str]]: history.append((message, '')) return history 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=bot, inputs=[ chatbot, max_new_tokens, temperature, top_p, system_prompt, ], outputs=chatbot, ) 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=msg, api_name=False, queue=False, ) clear.click(lambda: None, None, chatbot, queue=False) gr.Markdown(LICENSE) demo.queue(concurrency_count=1, max_size=16) demo.launch()