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1from peft import PeftModel
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4# Load base model
5base_model = AutoModelForCausalLM.from_pretrained(
6 "Qwen/Qwen2.5-Coder-3B-Instruct",
7 load_in_4bit=True,
8 device_map="auto"
9)
10tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
11
12# Load LoRA adapters
13model = PeftModel.from_pretrained(base_model, "Pavloffm/qwen-commit-lora")
14
15# Generate commit message
16diff = """diff --git a/src/main.py b/src/main.py
17index 1234567..abcdefg 100644
18--- a/src/main.py
19+++ b/src/main.py
20@@ -1,3 +1,5 @@
21+def new_feature():
22+ pass
23"""
24
25messages = [{"role": "user", "content": f"Generate a conventional commit message for this diff:\n{diff}"}]
26inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
27outputs = model.generate(inputs, max_new_tokens=100)
28print(tokenizer.decode(outputs[0], skip_special_tokens=True))