qwen3-4b-structeval-lora-57-merged/README.md

---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- daichira/structured-3k-mix-sft
- daichira/structured-5k-mix-sft
- daichira/structured-hard-sft-4k
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
---
# qwen3-4b-structured-output-merged

This repository provides a merged full model based on `Qwen/Qwen3-4B-Instruct-2507`, trained with LoRA for structured output tasks.

The model was fine-tuned with LoRA and then merged into the base model weights, so it can be loaded directly without PEFT.

## Training Objective

This model is optimized to improve structured output accuracy (JSON / YAML / XML / TOML / CSV).

Loss was applied only to the final assistant output, while intermediate reasoning (Chain-of-Thought) was masked during training.

## Training Configuration
Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit) → merged after training
Max sequence length: 2048
LoRA config (during training): r=64, alpha=128
Loss: applied only to the final assistant output (think tags always masked)
Code fences: applied to all samples from Step 2 onward

---

### Step 1

Learning rate: 1e-6
Dataset: daichira/structured-5k-mix-sft
Notes: Initial SFT. Think tags were excluded from the loss.

### Step 2

Learning rate: 3e-7
Dataset: daichira/structured-5k-mix-sft
Notes: Code fences were added to all samples.

### Step 3

Learning rate: 3e-7
Dataset: daichira/structured-5k-mix-sft
Notes: The following tasks were upsampled 2×:

* csv → json
* csv → xml
* csv → yaml

### Step 4

Learning rate: 3e-7
Dataset: daichira/structured-hard-sft-4k
Notes: Training on the hard dataset for robustness.

### Step 5

Learning rate: 1e-6
Dataset: daichira/structured-5k-mix-sft
Notes: Training focused only on:

* csv → json
* csv → xml

### Step 6

Learning rate: 1e-6
Dataset: daichira/structured-5k-mix-sft
Notes: Continued training on:

* csv → json
* csv → xml

### Step 7

Learning rate: 5e-7
Dataset: daichira/structured-hard-sft-4k
Notes: Focused training on:

* xml → yaml (hard)

### Step 8

Learning rate: 5e-7
Dataset: daichira/structured-hard-sft-4k
Notes: Focused training on:

* text → yaml (hard)
* xml → yaml (hard)

### Step 9

Learning rate: 5e-7
Dataset: daichira/structured-5k-mix-sft
Notes: Focused training on:

* csv → xml 

### Step 10

Learning rate: 1e-7
Dataset: daichira/structured-3k-mix-sft
Notes: Final stabilization step focused on:

* csv → xml 

## Usage

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

model_id = "kiratan/qwen3-4b-structeval-lora-57-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
```

## Sources & Terms (IMPORTANT)

Training data:
- `daichira/structured-3k-mix-sft`
- `daichira/structured-hard-sft-4k`
- `daichira/structured-5k-mix-sft`

Dataset License: CC-BY-4.0 License.

Compliance: Users must comply with the CC-BY-4.0 license terms for datasets and the base model's original terms of use.