---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
license: mit
tags:
- lora
- json
- structured-output
- text-generation
- qwen2
- sft
- trl
language:
- en
pipeline_tag: text-generation
---

# Qwen2.5-3B JSON Structured Output (LoRA)

A LoRA adapter that enhances Qwen2.5-3B-Instruct for extracting structured JSON from unstructured text.

## Training Details

| Parameter | Value |
|-----------|-------|
| Base Model | Qwen/Qwen2.5-3B-Instruct |
| Method | QLoRA (4-bit NF4) |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| Target Modules | q,k,v,o,gate,up,down |
| Trainable Params | 29.9M (1.76%) |
| Training Examples | 20 curated |
| Epochs | 5 |
| Learning Rate | 5e-5 |
| Final Loss | 1.506 |
| Hardware | NVIDIA RTX 3090 x2 |

## Quick Start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch, json

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct", quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(model, "2reb/Qwen2.5-3B-JSON-StructuredOutput")
```

## License

MIT