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import os
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
print("=== Application Starting (LoRA Mode with Quantization) ===")
try:
# 1. 設定 Base Model (基礎模型)
BASE_MODEL_ID = "QLU-NLP/BianCang-Qwen2.5-7B"
# 2. 自動偵測 Adapter (微調權重) 路徑
if os.path.exists("BianCang-Qwen2.5-7B-Instruct_finetuned_model_1"):
ADAPTER_PATH = "BianCang-Qwen2.5-7B-Instruct_finetuned_model_1"
else:
ADAPTER_PATH = "."
print(f"Base Model: {BASE_MODEL_ID}")
print(f"Adapter Path: {ADAPTER_PATH}")
# 3. 載入 Tokenizer
print("Loading Tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)
# 4. 載入 Base Model (使用 4-bit 量化以節省 VRAM，適合 T4 GPU)
print("Loading Base Model with 4-bit quantization...")
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
try:
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL_ID,
quantization_config=quantization_config,
device_map="auto",
trust_remote_code=True,
offload_folder="offload" # 加入 offload 資料夾以防萬一
)
except Exception as e:
print(f"GPU load failed: {e}. Fallback to CPU.")
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL_ID,
device_map="cpu",
trust_remote_code=True
)
# 5. 掛載 LoRA Adapter
print("Loading LoRA Adapter...")
try:
model = PeftModel.from_pretrained(
base_model,
ADAPTER_PATH,
offload_folder="offload" # 加入 offload 資料夾解決 Peft 報錯
)
print("LoRA Adapter loaded successfully!")
except Exception as e:
print(f"Failed to load adapter: {e}")
print("Running with Base Model only as fallback.")
model = base_model
def predict(message, history):
# 構建 Prompt
system_prompt = "你是一個專業的中醫藥材知識助手。你具備深厚的中醫理論基礎，特別擅長中藥材的性味、歸經、功效與主治。"
messages = [{"role": "system", "content": system_prompt}]
for human, assistant in history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": assistant})
messages.append({"role": "user", "content": message})
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
do_sample=True
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
# 建立 Gradio 介面
demo = gr.ChatInterface(
fn=predict,
title="BianCang-Qwen2.5-7B TCM Chatbot",
description="中醫藥材知識微調模型 (4-bit LoRA)"
)
if __name__ == "__main__":
# 移除 Gradio 4.x 不支援的 show_api 參數
demo.launch(server_name="0.0.0.0", server_port=7860)
except Exception as e:
print(f"!!! CRITICAL ERROR ===\n{e}\n======================")
raise e
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