--- license: mit base_model: unsloth/gemma-3-12b-it-qat-bnb-4bit tags: - kubernetes - devops - infrastructure - k8s - turkish - gemma - unsloth - lora datasets: - mcipriano/stackoverflow-kubernetes-questions - Szaid3680/Devops - ahmedgongi/Devops_LLM - HelloBoieeee/kubernetes_config - sidddddddddddd/kubernetes-with-ood - peterpanpan/stackoverflow-kubernetes-questions - dereklck/kubernetes_operator_3b_1.5k - dereklck/kubernetes_cli_dataset_20k library_name: peft language: - en - tr --- # Kubernetes AI - Gemma 3 12B LoRA Adapters Fine-tuned Gemma 3 12B model specialized for answering Kubernetes questions in Turkish. ## Model Description This model consists of LoRA adapters fine-tuned on `unsloth/gemma-3-12b-it-qat-bnb-4bit` using a comprehensive dataset of Kubernetes documentation, Stack Overflow questions, and DevOps scenarios. **Primary Purpose:** Answer Kubernetes-related questions in Turkish language. ### Use Cases - Kubernetes cluster management and troubleshooting - YAML configuration generation and validation - kubectl command assistance - Debugging pod, service, and deployment issues - Kubernetes best practices and concepts - DevOps workflow optimization - **Turkish language Kubernetes Q&A** ## Quick Start ### Loading the Model ```python from unsloth import FastLanguageModel from peft import PeftModel import torch # Load base Gemma 3 12B model model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/gemma-3-12b-it-qat-bnb-4bit", max_seq_length=2048, dtype=None, load_in_4bit=True, # Use 4-bit quantization to fit in GPU memory ) # Load Kubernetes AI LoRA adapters model = PeftModel.from_pretrained( model, "aciklab/kubernetes-ai" ) # Enable inference mode FastLanguageModel.for_inference(model) # Example usage (Turkish question) messages = [ {"role": "user", "content": "Kubernetes'te 3 replikaya sahip bir deployment nasıl oluştururum?"} ] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ).to("cuda") outputs = model.generate( input_ids=inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Example Questions ### Turkish Examples ```python # Deployment creation "Node.js uygulaması için 3 replika, sağlık kontrolleri ve kaynak limitleri olan bir Kubernetes deployment oluştur." # Troubleshooting "Pod'um CrashLoopBackOff durumunda. Yaygın nedenleri nelerdir ve nasıl debug ederim?" # kubectl commands "Production namespace'indeki çalışmayan tüm pod'ları gösteren kubectl komutunu yaz." # Best practices "Kubernetes'te container güvenliği için en iyi uygulamalar nelerdir?" # Service creation "LoadBalancer tipinde bir Kubernetes servisi nasıl yapılandırılır?" ``` ### English Examples ```python "How do I create a Kubernetes deployment with 3 replicas?" "What are the common causes of CrashLoopBackOff?" "Show me kubectl command to get all pods in production namespace." ``` ## Training Dataset The model was trained on **~157,000 examples** from multiple high-quality Kubernetes and DevOps datasets: | Dataset | Count | Description | |---------|----------|-------------| | **Kubernetes Official Documentation** | | | | - Concepts | 2,700 | Core Kubernetes concepts | | - Kubectl Reference | 600 | kubectl command documentation | | - Setup Guides | 430 | Installation and setup | | - Tasks | 4,300 | Practical task guides | | - Tutorials | 880 | Step-by-step tutorials | | **Stack Overflow** | | | | mcipriano/stackoverflow-kubernetes-questions | 30,000 | Kubernetes Q&A | | peterpanpan/stackoverflow-kubernetes-questions | 22,000 | Additional Kubernetes Q&A | | **DevOps Datasets** | | | | Szaid3680/Devops | 42,000 | General DevOps content | | ahmedgongi/Devops_LLM | 20,500 | Kubernetes-filtered DevOps (from 140k) | | **Configuration & Operations** | | | | HelloBoieeee/kubernetes_config | 10,000 | Kubernetes configurations | | sidddddddddddd/kubernetes-with-ood | 6,000 | Kubernetes scenarios (incl. Turkish translations) | | dereklck/kubernetes_cli_dataset_20k | 19,000 | kubectl CLI examples | | dereklck/kubernetes_operator_3b_1.5k | 1,800 | Kubernetes operator patterns | **Total Training Examples: ~157,210** ## Training Details - **Base Model**: unsloth/gemma-3-12b-it-qat-bnb-4bit - **Method**: LoRA (Low-Rank Adaptation) - **Framework**: Unsloth - **LoRA Rank**: 8 - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj - **Training Checkpoint**: checkpoint-8175 - **Max Sequence Length**: 1024 tokens - **Training Time**: 28 hours - **Hardware**: NVIDIA GeForce RTX 5070 12GB ## Hardware Requirements - **Minimum VRAM**: 12GB (with 4-bit quantization) - **Recommended VRAM**: 24GB (for faster inference) - **CPU RAM**: 32GB+ - **Training Hardware**: RTX 5070 12GB ## Limitations - May not have information on very recent Kubernetes features released after training - Primarily trained for **Turkish language** responses, though it can handle English queries - Best suited for technical Kubernetes questions; general conversation capabilities can be limited ## Performance Notes - Trained on RTX 5070 12GB in 28 hours - Works with 12GB VRAM using 4-bit quantization - Fast startup by loading only adapters without full model reload ## License This model is released under the **MIT License**. Free to use in commercial and open-source projects. ## Acknowledgments - Google and Unsloth team for the Gemma 3 base model - Unsloth team for the efficient training framework - All dataset contributors - Kubernetes community for comprehensive documentation - NVIDIA for RTX 5070 enabling 28-hour training ## Contact For questions or feedback, please open an issue on the model repository. --- **Note**: This is a LoRA adapter, not a full model. You must load it on top of `unsloth/gemma-3-12b-it-qat-bnb-4bit` to use it. ## Related Links - [Unsloth Documentation](https://docs.unsloth.ai/) - [Gemma Model Card](https://ai.google.dev/gemma) - [PEFT Documentation](https://huggingface.co/docs/peft) - [Kubernetes Documentation](https://kubernetes.io/docs/) ## Citations ### Datasets ```bibtex @misc{stackoverflow-kubernetes-mcipriano, author = {mcipriano}, title = {Stack Overflow Kubernetes Questions}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/mcipriano/stackoverflow-kubernetes-questions} } @misc{devops-szaid, author = {Szaid3680}, title = {DevOps Dataset}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/Szaid3680/Devops} } @misc{devops-llm-ahmed, author = {ahmedgongi}, title = {DevOps LLM Dataset}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/ahmedgongi/Devops_LLM} } @misc{kubernetes-config-hello, author = {HelloBoieeee}, title = {Kubernetes Config Dataset}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/HelloBoieeee/kubernetes_config} } @misc{kubernetes-ood-sidddddddddddd, author = {sidddddddddddd}, title = {Kubernetes with OOD Dataset}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/sidddddddddddd/kubernetes-with-ood} } @misc{stackoverflow-kubernetes-peter, author = {peterpanpan}, title = {Stack Overflow Kubernetes Questions}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/peterpanpan/stackoverflow-kubernetes-questions} } @misc{kubernetes-operator-derek, author = {dereklck}, title = {Kubernetes Operator Dataset}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/dereklck/kubernetes_operator_3b_1.5k} } @misc{kubernetes-cli-derek, author = {dereklck}, title = {Kubernetes CLI Dataset}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/dereklck/kubernetes_cli_dataset_20k} } ``` ### Model ```bibtex @misc{kubernetes-ai, author = {aciklab}, title = {Kubernetes AI Turkish - Gemma 3 12B LoRA Adapters}, year = {2025}, publisher = {HuggingFace}, url = {https://huggingface.co/aciklab/kubernetes-ai}, note = {Trained on RTX 5070 12GB in 28 hours} } ```