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:
Step 6
Learning rate: 1e-6
Dataset: daichira/structured-5k-mix-sft
Notes: Continued training on:
Step 7
Learning rate: 5e-7
Dataset: daichira/structured-hard-sft-4k
Notes: Focused training on:
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:
Step 10
Learning rate: 1e-7
Dataset: daichira/structured-3k-mix-sft
Notes: Final stabilization step focused on:
Usage
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model_id = "kiratan/qwen3-4b-structeval-lora-57-merged"
5
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 torch_dtype=torch.float16,
10 device_map="auto",
11)
Sources & Terms (IMPORTANT)
Training data:
daichira/structured-3k-mix-sftdaichira/structured-hard-sft-4kdaichira/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.
