Kazupal
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structeval-lora-qwen3-4b

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----
-base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
-library_name: peft
----
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-### Framework versions
-- PEFT 0.13.2

+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: peft
+tags:
+- lora
+- qlora
+- structeval
+- structured-output
+- matsuolab
+---
+# StructEval-Oriented Qwen3-4B LoRA (KazumaInoue)
+## 1. Model Overview
+This repository provides a LoRA adapter trained for structured output generation tasks
+based on Qwen/Qwen3-4B-Instruct-2507.
+The model was developed as part of the Matsuo Lab LLM Application Course (Final Assignment).
+Its primary focus is improving output stability when generating structured formats such as
+JSON, YAML, and XML under explicit formatting constraints.
+## 2. Training Objective and Design Rationale
+The objective of this model is to generate outputs that follow explicit structural constraints
+specified in the prompt.
+To achieve this, the following design choices were adopted:
+- Loss is applied only to assistant responses (assistant-only loss).
+- Output-marker-based masking is used to avoid learning intermediate reasoning tokens.
+- Priority is given to format correctness rather than creative or verbose responses.
+## 3. Training Configuration
+- Base model: Qwen/Qwen3-4B-Instruct-2507
+- Training method: QLoRA (4-bit quantized base model with LoRA adapters)
+- Maximum sequence length: 512
+- LoRA rank (r): 64
+- LoRA alpha: 128
+- LoRA dropout: 0.05
+- Number of epochs: 1
+- Optimizer and scheduler: AdamW with cosine learning rate schedule
+- Training framework: Unsloth, PEFT, Hugging Face Transformers
+## 4. Training Data
+The model was trained using a synthetic supervised fine-tuning dataset provided by the course organizers.
+- Dataset: u-10bei/structured_data_with_cot_dataset_512_v2
+- Dataset type: Structured output SFT dataset
+- Content: Instruction-following samples with explicit output markers
+### Dataset License and Compliance
+The dataset was used strictly in accordance with the course guidelines and is intended
+for research and educational purposes only.
+## 5. Usage
+This repository contains LoRA adapter weights only.
+The base model must be obtained separately.
+Example usage with Unsloth:
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="Qwen/Qwen3-4B-Instruct-2507",
+    load_in_4bit=True,
+)
+model.load_adapter("YOUR_HF_REPO_ID")