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import os |
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from threading import Thread |
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from typing import Iterator |
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import gradio as gr |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 1024 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
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description = """ |
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<h1><center>LLM Finetuned on TaoScience<center></h1> |
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<h3><center>TaoGPT is a fine-tuned LLM on Tao Science by Dr. Rulin Xu and Dr. Zhi Gang Sha. <br> Check out- <a href='https://github.com/agencyxr/taogpt7B'>Github Repo</a> For More Information. 💬<h3><center> |
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""" |
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NOMIC = """ |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>TaoGPT - DataMap</title> |
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<style> |
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iframe { |
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width: 100%; |
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height: 600px; /* You can adjust the height as needed */ |
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border: 0; |
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} |
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</style> |
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</head> |
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<body> |
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<iframe |
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src="https://atlas.nomic.ai/map/c1ce06f4-7ed0-4c02-88a4-dd3b47bdf878/f2941fb8-0f36-4a23-8cbe-40dbf76ca9e4?xs=-41.09135&xf=41.12038&ys=-22.50394&yf=23.67273" |
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></iframe> |
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</body> |
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</html> |
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""" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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if torch.cuda.is_available(): |
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model_id = "agency888/TaoGPT-v1" |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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tokenizer.use_default_system_prompt = False |
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@spaces.GPU |
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def generate( |
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message: str, |
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chat_history: list[tuple[str, str]], |
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system_prompt: str, |
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max_new_tokens: int = 1024, |
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temperature: float = 0.6, |
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top_p: float = 0.9, |
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top_k: int = 50, |
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repetition_penalty: float = 1.2, |
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) -> Iterator[str]: |
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conversation = [] |
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if system_prompt: |
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conversation.append({"role": "system", "content": system_prompt}) |
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for user, assistant in chat_history: |
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conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.encode(f"<s>{system_prompt}{message}", return_tensors="pt") |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
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input_ids = input_ids.to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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top_p=top_p, |
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top_k=top_k, |
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temperature=temperature, |
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num_beams=1, |
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repetition_penalty=repetition_penalty, |
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pad_token_id=2, |
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eos_token_id=2, |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs ) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs) |
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chat_interface = gr.ChatInterface( |
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fn=generate, |
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additional_inputs=[ |
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gr.Textbox(label="System prompt", lines=6), |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=4.0, |
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step=0.1, |
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value=0.6, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.9, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=50, |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1.2, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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["What is TaoScience ?"], |
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["TaoScience was written by ?"], |
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["Tell me more about TaoScience"]], |
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) |
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with gr.Blocks() as demo: |
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gr.HTML(description) |
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chat_interface.render() |
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with gr.Column(): |
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with gr.Accordion(label="Visualise Training Data" ,open=False): |
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gr.HTML(NOMIC) |
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gr.Markdown("The model is prone to Hallucination and many not always be Factual") |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch(share=True) |
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