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language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- spring-boot
- java
- code
- fine-tuned
- qlora
- peft
pipeline_tag: text-generation
---
# Spring Boot Assistant
A fine-tuned version of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/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](https://huggingface.co/datasets/sahil2801/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
```python
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 |