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.gitattributes

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+kubernetes-ai-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
+kubernetes-ai-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+kubernetes-ai-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- tr
+- en
+library_name: gguf
+tags:
+- kubernetes
+- devops
+- quantized
+- gguf
+- gemma3
+- llama-cpp
+- ollama
+base_model: aciklab/kubernetes-ai
+model_type: gemma3
+quantized_by: aciklab
+---
+
+# Kubernetes AI - GGUF Quantized Models
+
+Fine-tuned Gemma 3 12B model specialized for answering Kubernetes questions in Turkish, quantized to GGUF format for efficient local inference.
+
+## Model Description
+
+This repository contains GGUF quantized versions of the Kubernetes AI model, optimized for running on consumer hardware without GPU requirements. The model consists of LoRA adapters fine-tuned on unsloth/gemma-3-12b-it-qat-bnb-4bit and converted to GGUF format for llama.cpp compatibility.
+
+**Primary Purpose:** Answer Kubernetes-related questions in Turkish language on local machines.
+
+## Available Models
+
+| Model | Size | Download |
+|-------|------|----------|
+| **Unquantized** | 22.0 GB | [kubernetes-ai.gguf](https://huggingface.co/aciklab/kubernetes-ai-GGUF/resolve/main/kubernetes-ai.gguf) |
+| **Q4_K_M** | 7.3 GB | [kubernetes-ai-Q4_K_M.gguf](https://huggingface.co/aciklab/kubernetes-ai-GGUF/resolve/main/kubernetes-ai-Q4_K_M.gguf) |
+| **Q4_K_S** | 6.9 GB | [kubernetes-ai-Q4_K_S.gguf](https://huggingface.co/aciklab/kubernetes-ai-GGUF/resolve/main/kubernetes-ai-Q4_K_S.gguf) |
+| **Q3_K_M** | 6.0 GB | [kubernetes-ai-Q3_K_M.gguf](https://huggingface.co/aciklab/kubernetes-ai-GGUF/resolve/main/kubernetes-ai-Q3_K_M.gguf) |
+| **IQ3_M** | 5.6 GB | [kubernetes-ai-IQ3_M.gguf](https://huggingface.co/aciklab/kubernetes-ai-GGUF/resolve/main/kubernetes-ai-IQ3_M.gguf) |
+
+**Recommended:** Q4_K_M for best balance of quality and size, or IQ3_M for low-end systems.
+
+## Quick Start
+
+### Using Ollama (Recommended)
+
+Ollama provides the easiest way to run GGUF models locally.
+
+#### 1. Install Ollama
+
+```bash
+# Linux
+curl -fsSL https://ollama.com/install.sh | sh
+
+# macOS
+brew install ollama
+
+# Windows - Download from https://ollama.com/download
+```
+
+#### 2. Download Model
+
+```bash
+# Download your preferred quantization
+wget https://huggingface.co/aciklab/kubernetes-ai-GGUF/resolve/main/kubernetes-ai-Q4_K_M.gguf
+```
+
+#### 3. Create Modelfile
+
+```bash
+cat > Modelfile << 'EOF'
+FROM <path-to-model>/kubernetes-ai.gguf
+
+TEMPLATE """{{ if .System }}<start_of_turn>system
+{{ .System }}<end_of_turn>
+{{ end }}{{ if .Prompt }}<start_of_turn>user
+{{ .Prompt }}<end_of_turn>
+{{ end }}<start_of_turn>model
+{{ .Response }}<end_of_turn>
+"""
+
+# Model Parametreleri
+PARAMETER temperature 1.0
+PARAMETER top_p 0.95
+PARAMETER top_k 64
+PARAMETER repeat_penalty 1.05
+PARAMETER stop "<start_of_turn>"
+PARAMETER stop "<end_of_turn>"
+
+SYSTEM """Sen Kubernetes konusunda uzmanlaşmış bir yapay zeka asistanısın. Kubernetes ile ilgili soruları Türkçe olarak yanıtlıyorsun."""
+EOF
+```
+
+#### 4. Create and Run Model
+
+```bash
+# Create model
+ollama create kubernetes-ai -f Modelfile
+
+# Run interactive chat
+ollama run kubernetes-ai
+
+# Example query
+ollama run kubernetes-ai "Kubernetes'te 3 replikaya sahip bir deployment nasıl oluştururum?"
+```
+
+## Training Details
+
+This model is based on the [aciklab/kubernetes-ai](https://huggingface.co/aciklab/kubernetes-ai) LoRA adapters:
+
+- **Base Model:** unsloth/gemma-3-12b-it-qat-bnb-4bit
+- **Training Method:** LoRA (Low-Rank Adaptation)
+- **LoRA Rank:** 8
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training Dataset:** ~157,210 examples from Kubernetes docs, Stack Overflow, and DevOps datasets
+- **Training Time:** 28 hours on NVIDIA RTX 5070 12GB
+- **Max Sequence Length:** 1024 tokens
+
+### Training Dataset Summary
+
+| Dataset Category | Count | Description |
+|-----------------|-------|-------------|
+| **Kubernetes Official Docs** | 8,910 | Concepts, kubectl, setup, tasks, tutorials |
+| **Stack Overflow** | 52,000 | Kubernetes Q&A from community |
+| **DevOps Datasets** | 62,500 | General DevOps and Kubernetes content |
+| **Configurations & CLI** | 36,800 | Kubernetes configs, kubectl examples, operators |
+| **Total** | **~157,210** | Comprehensive Kubernetes knowledge base |
+
+## Quantization Details
+
+All models were quantized using llama.cpp with importance matrix optimization:
+
+- **Source:** Merged LoRA adapters with base model
+- **Quantization Tool:** llama.cpp (latest)
+- **Method:** K-quant and IQ-quant mixtures
+- **Optimization:** Importance matrix for better quality
+
+### Quantization Quality
+
+- **Q4_K_M:** Best balance - recommended for most users
+- **Q4_K_S:** Slightly smaller with minimal quality loss
+- **Q3_K_M:** Good for memory-constrained systems
+- **IQ3_M:** Advanced 3-bit quantization for laptops
+- **Unquantized:** Original F16/F32 precision
+
+## Hardware Requirements
+
+### Minimum
+- **CPU:** 4+ cores
+- **RAM:** 8GB (for IQ3_M/Q3_K_M quantizations)
+- **Storage:** 6-8GB free space
+- **GPU:** Not required (CPU inference)
+
+### Recommended
+- **CPU:** 8+ cores
+- **RAM:** 16GB (for Q4_K_M/Q4_K_S quantizations)
+- **Storage:** 10GB free space
+- **GPU:** Optional (can accelerate inference)
+
+## License
+
+This model is released under the **MIT License**. Free to use in commercial and open-source projects.
+
+## Contact
+
+**Produced by:** HAVELSAN/Açıklab
+
+For questions or feedback, please open an issue on the model repository.
+
+---
+
+**Note:** These are GGUF quantized versions ready for immediate use. No additional model loading or merging required.

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