Add model card

README.md

@@ -0,0 +1,65 @@
+---
+license: apache-2.0
+library_name: llama-cpp-python
+tags:
+  - llama
+  - instruction-tuned
+  - thai
+  - gguf
+  - quantized
+  - q8
+  - rag
+  - chatbot
+language:
+  - th
+---
+
+# Llama 3.2 Typhoon2 3B Instruct (GGUF Q8_0)
+
+Fine-tuned Thai instruction-following model quantized to GGUF Q8_0 format for efficient inference.
+
+## Model Details
+
+- **Base Model**: typhoon-ai/llama3.2-typhoon2-3b-instruct
+- **Format**: GGUF (Q8_0 quantization)
+- **Parameters**: 3 billion
+- **Language**: Thai
+- **Use Case**: Context-aware Q&A, RAG systems, chatbots
+
+## Training
+
+- **Framework**: Unsloth
+- **Method**: Supervised Fine-Tuning (SFT)
+- **Training Data**: Thai instruction-following dataset with negative samples for strictness
+- **Optimization**: LoRA + 4-bit quantization during training
+
+## Inference
+
+### Using llama-cpp-python
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="model.gguf",
+    n_ctx=4096,
+    n_gpu_layers=0,
+)
+
+response = llm(prompt, max_tokens=256, temperature=0.0)
+```
+
+### Docker Deployment (EKS)
+
+See deployment guide in the chat-inference Helm chart.
+
+## Performance
+
+- **Quantization**: Q8_0 (8-bit)
+- **Model Size**: ~3.3 GB
+- **Inference Speed (CPU)**: ~2-5 tokens/sec (t3.xlarge)
+- **Recommended CPU**: 2-4 cores, 4-6 GB RAM
+
+## License
+
+Apache License 2.0