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--- |
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base_model: Qwen/Qwen3-4B-Instruct-2507 |
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datasets: |
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- u-10bei/structured_data_with_cot_dataset_512_v2 |
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language: |
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- en |
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- ja |
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license: apache-2.0 |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- qlora |
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- lora |
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- structured-output |
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- structeval |
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--- |
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# Qwen3-4B StructEval exp006 - structured_data_with_cot_dataset_512_v2 |
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This repository provides a **LoRA adapter** fine-tuned from |
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**Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**. |
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**This repository contains LoRA adapter weights only**. |
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The base model must be loaded separately. |
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## Training Objective |
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This adapter is trained to improve **structured output accuracy** |
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(JSON / YAML / XML / TOML / CSV). |
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Loss is applied only to the final assistant output, |
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while intermediate reasoning (Chain-of-Thought) is masked. |
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## Training Configuration |
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- **Experiment ID**: exp006 |
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- **Base model**: Qwen/Qwen3-4B-Instruct-2507 |
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- **Training dataset**: u-10bei/structured_data_with_cot_dataset_512_v2 |
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- **Method**: QLoRA (4-bit) |
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- **Max sequence length**: 512 |
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- **Epochs**: 2 |
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- **Learning rate**: 1e-06 |
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- **LoRA parameters**: r=8, alpha=8 |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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import torch |
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base = "Qwen/Qwen3-4B-Instruct-2507" |
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adapter = "junfukuda/qwen3-structeval-exp006-u10bei" |
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tokenizer = AutoTokenizer.from_pretrained(base) |
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model = AutoModelForCausalLM.from_pretrained( |
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base, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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model = PeftModel.from_pretrained(model, adapter) |
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``` |
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## Sources & Terms (IMPORTANT) |
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**Training data**: u-10bei/structured_data_with_cot_dataset_512_v2 |
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**Dataset License**: The dataset used for training is subject to its original license terms. |
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Please refer to the dataset repository for specific license information. |
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**Compliance**: Users must comply with both the dataset's license terms and the base model's original terms of use. |
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## Competition Context |
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This model was developed as part of the StructEval competition, focusing on accurate structured output generation. |
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