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| import os | |
| import gradio as gr | |
| from text_generation import Client | |
| from conversation import get_default_conv_template | |
| from transformers import AutoTokenizer | |
| from pymongo import MongoClient | |
| DB_NAME = os.getenv("MONGO_DBNAME", "taiwan-llm") | |
| USER = os.getenv("MONGO_USER") | |
| PASSWORD = os.getenv("MONGO_PASSWORD") | |
| uri = f"mongodb+srv://{USER}:{PASSWORD}@{DB_NAME}.kvwjiok.mongodb.net/?retryWrites=true&w=majority" | |
| mongo_client = MongoClient(uri) | |
| db = mongo_client[DB_NAME] | |
| conversations_collection = db['conversations'] | |
| DESCRIPTION = """ | |
| # Language Models for Taiwanese Culture | |
| <p align="center"> | |
| ✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a> | |
| • | |
| 🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a> | |
| • 👨️ <a href="https://github.com/MiuLab/Taiwan-LLaMa/tree/main" target="_blank">Github Repo</a> | |
| <br/><br/> | |
| <img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <br/> | |
| </p> | |
| Taiwan-LLaMa is a fine-tuned model specifically designed for traditional mandarin applications. It is built upon the LLaMa 2 architecture and includes a pretraining phase with over 5 billion tokens and fine-tuning with over 490k multi-turn conversational data in Traditional Mandarin. | |
| ## Key Features | |
| 1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications. | |
| 2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses. | |
| 3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture. | |
| 4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo. | |
| ## Model Versions | |
| Different versions of Taiwan-LLaMa are available: | |
| - **Taiwan-LLaMa v2.0 (This demo)**: Cleaner pretraining, Better post-training | |
| - **Taiwan-LLaMa v1.0**: Optimized for Taiwanese Culture | |
| - **Taiwan-LLaMa v0.9**: Partial instruction set | |
| - **Taiwan-LLaMa v0.0**: No Traditional Mandarin pretraining | |
| The models can be accessed from the provided links in the Hugging Face repository. | |
| Try out the demo to interact with Taiwan-LLaMa and experience its capabilities in handling Traditional Mandarin! | |
| """ | |
| LICENSE = """ | |
| ## Licenses | |
| - Code is licensed under Apache 2.0 License. | |
| - Models are licensed under the LLAMA 2 Community License. | |
| - By using this model, you agree to the terms and conditions specified in the license. | |
| - By using this demo, you agree to share your input utterances with us to improve the model. | |
| ## Acknowledgements | |
| Taiwan-LLaMa project acknowledges the efforts of the [Meta LLaMa team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) in democratizing large language models. | |
| """ | |
| DEFAULT_SYSTEM_PROMPT = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. You are built by NTU Miulab by Yen-Ting Lin for research purpose." | |
| endpoint_url = os.environ.get("ENDPOINT_URL", "http://127.0.0.1:8080") | |
| client = Client(endpoint_url, timeout=120) | |
| eos_token = "</s>" | |
| MAX_MAX_NEW_TOKENS = 1024 | |
| DEFAULT_MAX_NEW_TOKENS = 1024 | |
| max_prompt_length = 4096 - MAX_MAX_NEW_TOKENS - 10 | |
| model_name = "yentinglin/Taiwan-LLM-7B-v2.0-chat" | |
| 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=1, | |
| maximum=MAX_MAX_NEW_TOKENS, | |
| step=1, | |
| value=DEFAULT_MAX_NEW_TOKENS, | |
| ) | |
| temperature = gr.Slider( | |
| label='Temperature', | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.7, | |
| ) | |
| 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=50, | |
| ) | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot(history, max_new_tokens, temperature, top_p, top_k, system_prompt): | |
| conv = get_default_conv_template("vicuna").copy() | |
| roles = {"human": conv.roles[0], "gpt": conv.roles[1]} # map human to USER and gpt to ASSISTANT | |
| conv.system = system_prompt | |
| for user, bot in history: | |
| conv.append_message(roles['human'], user) | |
| conv.append_message(roles["gpt"], bot) | |
| msg = conv.get_prompt() | |
| prompt_tokens = tokenizer.encode(msg) | |
| length_of_prompt = len(prompt_tokens) | |
| if length_of_prompt > max_prompt_length: | |
| msg = tokenizer.decode(prompt_tokens[-max_prompt_length + 1:]) | |
| history[-1][1] = "" | |
| for response in client.generate_stream( | |
| msg, | |
| max_new_tokens=max_new_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=top_k, | |
| ): | |
| if not response.token.special: | |
| character = response.token.text | |
| history[-1][1] += character | |
| yield history | |
| # After generating the response, store the conversation history in MongoDB | |
| conversation_document = { | |
| "model_name": model_name, | |
| "history": history, | |
| "system_prompt": system_prompt, | |
| "max_new_tokens": max_new_tokens, | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "top_k": top_k, | |
| } | |
| conversations_collection.insert_one(conversation_document) | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| fn=bot, | |
| inputs=[ | |
| chatbot, | |
| max_new_tokens, | |
| temperature, | |
| top_p, | |
| top_k, | |
| 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, | |
| top_k, | |
| 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, | |
| top_k, | |
| 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=4, max_size=128) | |
| demo.launch() |