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Model: Mindie/Qwen3-4b-kss-style-tuning
Source: Original Platform

README.md

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+---
+library_name: transformers
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+datasets:
+- Mindie/Summary_article_KSS_style_dataset
+---
+
+```markdown
+> Instruction-tuned model with LoRA can enforce structured output without losing general knowledge.
+
+# KSS-Format Instruction-Tuned Model (LoRA)
+
+## 📌 Model Description
+
+This model is instruction-tuned to generate summaries in a structured format:
+
+Subject:
+Keywords:
+Summary:
+
+The goal of this project is to enforce output format while preserving the base model’s general knowledge and reasoning ability.
+
+---
+
+## 🎯 Key Features
+
+- ✅ Structured summary generation (KSS-style format)
+- ✅ Instruction-following behavior
+- ✅ Knowledge preservation after fine-tuning
+- ✅ Robust across both short and long inputs
+
+---
+
+## 🧠 Base Model
+
+- Base model: Qwen3-4B
+- Fine-tuning method: LoRA (Low-Rank Adaptation)
+
+---
+
+## 🏗️ Training Details
+
+### Dataset
+
+- Total samples: 596
+- Contrastive dataset:
+  - Instruction data (format enforced)
+  - Non-instruction data (free-form output)
+
+### Data Sources
+
+- GPT-generated summaries (short-form)
+- Base model-generated summaries (long-form)
+- CNN article dataset (961 samples used for label generation)
+
+---
+
+## ⚙️ Training Setup
+
+- Method: LoRA fine-tuning
+- Objective:
+  - Learn when to apply structured format
+  - Avoid overfitting to instruction-only behavior
+
+---
+
+## 📊 Evaluation
+
+### 1. Format Adherence
+
+- Evaluated on 50 samples
+- Result:
+  - High consistency in following required format
+
+---
+
+### 2. Knowledge Preservation (MMLU)
+
+- Evaluation method: Logit-based scoring
+- Samples: 20 per subject
+
+| Model        | Score |
+|--------------|------|
+| Base Model   | 0.725 |
+| Tuned Model  | 0.724 |
+
+👉 No significant performance degradation observed
+
+---
+
+## 💡 Key Insight
+
+Instruction tuning can enforce output structure **without degrading model knowledge**,  
+when using a carefully designed LoRA fine tuning setup.
+
+---
+
+## 🧪 Example Usage
+This model is finetuned on non-thinking mode. non-thinking mode is recommended
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+#model calling
+model_name = "Mindie/Qwen3-4b-kss-style-tuning"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+#generation
+prompt = text
+messages = [
+    {"role": "user", "content": f'Use KSS style Summaries: {prompt}'}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=200
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
+print(content)
+
+###Ouptut foramt
+Subject:
+Keywords:
+Summary:
+
+
+## 🔗 Project Links
+
+- GitHub Repository: https://github.com/byeongmin1/qwen3-4b-lora-instruction-tuning
+
+## 📖 Training Details
+
+This model was trained using a full pipeline including:
+- data generation
+- filtering
+- instruction tuning (LoRA)
+- Logit Evaluation (MMLU)
+
+For full implementation details, please refer to the GitHub repository.