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Running on Zero
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Create app.py
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app.py
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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
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from peft import PeftModel
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import os
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BASE_MODEL = "Qwen/Qwen2.5-Math-7B-Instruct"
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ADAPTER_REPO = "billwang37/mathbio-qwen-7b"
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HF_TOKEN = os.environ.get("HF_TOKEN")
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print("Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(ADAPTER_REPO, token=HF_TOKEN)
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print("Loading base model...")
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base_model = AutoModelForCausalLM.from_pretrained(
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BASE_MODEL,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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print("Loading LoRA adapter...")
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model = PeftModel.from_pretrained(base_model, ADAPTER_REPO, token=HF_TOKEN)
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model.eval()
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print("Model ready.")
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SYSTEM_PROMPT = "You are MathBioAgent, an expert AI assistant specialized in mathematical biology, epidemiology, operator learning, and partial differential equations."
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@spaces.GPU(duration=60)
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def chat(message, history):
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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for h in history:
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messages.append({"role": "user", "content": h[0]})
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messages.append({"role": "assistant", "content": h[1]})
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messages.append({"role": "user", "content": message})
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.3,
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do_sample=True,
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top_p=0.9,
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)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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return response
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demo = gr.ChatInterface(
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fn=chat,
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title="MathBio AI — Mathematical Biology Assistant",
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description="A specialized LLM for mathematical biology, epidemic modeling, PDEs, and operator learning. Fine-tuned on 27K arxiv-derived examples.",
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examples=[
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"What is R0 for an SIR model with beta=0.4 and gamma=0.1?",
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"Derive the stability condition for the SEIR endemic equilibrium.",
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"Explain the Keller-Segel chemotaxis model.",
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],
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)
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demo.launch()
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