> 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.
