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Model: kikansha-Tomasu/Qwen3-4B-Instruct-2507-sft1
Source: Original Platform

README.md

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+---
+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
+- sft
+---
+
+# Qwen3-4B-Instruct-2507-sft1
+
+This repository provides a **merged model** fine-tuned from
+**Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.
+
+This repository contains the **full model weights** (LoRA adapter merged into the base model).
+You can use this model directly without loading the base model separately.
+
+## Training Objective
+
+This adapter is trained to improve **structured output accuracy**
+(JSON / YAML / XML / TOML / CSV).
+
+Loss is applied only to the final assistant output,
+while intermediate reasoning (Chain-of-Thought) is masked.
+
+## 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
+import torch
+
+model_id = "kikansha-Tomasu/Qwen3-4B-Instruct-2507-sft1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+```
+
+## 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.