66 lines
1.6 KiB
Markdown
66 lines
1.6 KiB
Markdown
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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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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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---
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＜【課題】ここは自分で記入して下さい＞
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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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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: QLoRA (4-bit)
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- Max sequence length: 512
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- Epochs: 1
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- Learning rate: 3e-06
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- LoRA: r=64, alpha=128
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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 = "your_id/your-repo"
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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: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
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