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TaoGPT-7b

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AdithyaSK commited on Dec 29, 2023

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a916ab9

1 Parent(s): 9f47d1c

Create app.py

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