Update model card

README.md

@@ -6,4 +6,83 @@ base_model:
 - unsloth/Llama-3.2-3B-Instruct-bnb-4bit
 pipeline_tag: text-generation
 ---
-Korean instruction-following SLM fine-tuned from Llama-3.2-3B base using SFT and released in GGUF format for on-premise inference.
+
+### Model Summary
+
+This model is a **Korean instruction-following Small Language Model (SLM)** fine-tuned from the **Llama-3.2-3B base model** using **Supervised Fine-Tuning (SFT)**.
+The objective of this model is to validate a **resource-efficient fine-tuning and deployment pipeline** suitable for **on-premise and constrained GPU/CPU environments**, rather than to maximize benchmark scores.
+
+---
+
+### Training Approach
+
+* **Base Model**: Meta Llama-3.2-3B (base, non-instruct)
+* **Fine-Tuning Method**: Supervised Fine-Tuning (SFT)
+* **Parameter-Efficient Training**: LoRA (PEFT)
+* **Quantization During Training**: 4-bit (QLoRA)
+* **Training Framework**: Unsloth + Hugging Face TRL
+* **Training Environment**: Single GPU (Google Colab, Tesla T4)
+
+The model was trained using an **instruction–response prompt template (Alpaca-style)**, enabling stable instruction-following behavior in Korean.
+The fine-tuning process focused on **maintaining the base model’s general language capability while adapting response style, tone, and instruction compliance**.
+
+---
+
+### Dataset
+
+* **Primary Dataset**: `korean_safe_conversation`
+* **Language**: Korean
+* **Data Type**: Instruction–response conversational data
+* **Data Scale**: ~27K samples
+
+The dataset was preprocessed to ensure:
+
+* Clear separation between instruction and response
+* Explicit end-of-sequence (EOS) control to prevent uncontrolled generation
+* Consistent prompt formatting for stable training behavior
+
+---
+
+### Intended Use
+
+This model is intended for:
+
+* Korean instruction-following assistants
+* Domain-adapted SLM experimentation
+* On-premise inference scenarios where:
+
+  * Data privacy is critical
+  * GPU resources are limited
+  * Low-latency local inference is preferred
+
+Typical application examples include:
+
+* Internal enterprise assistants
+* Document-based Q&A systems (pre/post-RAG)
+* Operational report generation from structured or semi-structured text
+
+---
+
+### Deployment
+
+* **Format**: GGUF
+* **Quantization**: Q8
+* **Deployment Target**: CPU or low-VRAM environments
+* **Distribution**: Hugging Face Hub
+
+The GGUF format allows the model to be deployed **without external API dependencies**, making it suitable for **secure, offline, or air-gapped environments**.
+
+---
+
+### Limitations
+
+* This model is **not an official Meta Instruct model**
+* Preference optimization methods such as DPO or RLHF were not applied
+* The model was trained for **behavior adaptation and stability**, not for benchmark optimization
+* Performance may vary outside the instruction-following and conversational domains
+
+---
+
+### Technical Motivation
+
+This project demonstrates that **domain-adapted instruction-following models can be efficiently built and deployed using small-scale resources**, providing a practical alternative to large, cost-intensive LLM deployments in real-world systems.