README.md

---
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}
}
```