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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 | |
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 Chinese 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 Chinese. | |
## Key Features | |
1. **Traditional Chinese Support**: The model is fine-tuned to understand and generate text in Traditional Chinese, 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 v1.0 (This demo)**: Optimized for Taiwanese Culture | |
- **Taiwan-LLaMa v0.9**: Partial instruction set | |
- **Taiwan-LLaMa v0.0**: No Traditional Chinese 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 Chinese! | |
""" | |
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 | |
tokenizer = AutoTokenizer.from_pretrained("yentinglin/Taiwan-LLaMa-v1.0") | |
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 | |
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(max_size=128) | |
demo.launch() | |