Break0635/springboot-assistant

Spring Boot Assistant

A fine-tuned version of Qwen2.5-1.5B-Instruct specialized for Spring Boot and Java development questions.

Model Description

This model was fine-tuned using QLoRA (Quantized Low-Rank Adaptation) on a combination of Java code examples from CodeAlpaca-20k and custom Spring Boot question-answer pairs. It is designed to assist developers with Spring Boot concepts, patterns, and best practices.

Training Details

Parameter	Value
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Fine-tuning Method	QLoRA (LoRA + float16)
LoRA Rank (r)	16
LoRA Alpha	32
Training Epochs	3
Batch Size	2
Learning Rate	2e-4
LR Scheduler	Cosine
Hardware	Apple Silicon (MPS)

Training Data

• CodeAlpaca-20k — filtered for Java examples (~200 samples)
• Custom Spring Boot Q&A pairs covering:
  • Dependency Injection
  • REST API development
  • Spring annotations
  • Exception handling
  • Database configuration
  • Security with JWT
  • Testing with @SpringBootTest

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-1.5B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

# Load fine-tuned weights
model = PeftModel.from_pretrained(
    base_model,
    "Break0635/springboot-assistant"
)

tokenizer = AutoTokenizer.from_pretrained(
    "Qwen/Qwen2.5-1.5B-Instruct",
    trust_remote_code=True,
)

# Generate answer
def ask(question: str) -> str:
    prompt = f"### Instruction:\n{question}\n\n### Response:\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    
    generated = outputs[0][inputs["input_ids"].shape[1]:]
    return tokenizer.decode(generated, skip_special_tokens=True).strip()

# Example usage
answer = ask("What is dependency injection in Spring Boot?")
print(answer)

Example Questions