---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v2
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
- json
- no-cot
---

# Qwen3-4B Structured Output LoRA (No-CoT / Strict-JSON)

This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.

This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.

## Training Objective

This adapter is trained to improve **structured output accuracy**
(JSON / YAML / XML / TOML / CSV) and strict format compliance.

**Key Training Decisions:**
1.  **System Prompts Removed:** The model is trained without system prompts to match the inference environment where they are unavailable.
2.  **CoT Removed (Direct Output):** Chain-of-Thought (reasoning steps) and "Output:" markers were physically removed from the training data.
3.  **Assistant-Only Loss:** The model is trained to output the structured data **immediately** after the user prompt, with loss applied only to the output.

## Training Configuration

- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: QLoRA (4-bit)
- Max sequence length: 512
- Epochs: 1
- Learning rate: 1e-06
- LoRA: r=64, alpha=128

## Usage

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

base_model_name = "Qwen/Qwen3-4B-Instruct-2507"
adapter_name = "your_id/your-repo" # Replace with your HF hub path

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_name)

# Inference Example
messages = [
    {"role": "user", "content": "Convert this text to JSON: ..."}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

# The model will output JSON immediately
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Sources & Terms (IMPORTANT)
Training data: u-10bei/structured_data_with_cot_dataset_512_v2

Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License. 
Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.