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README.md

@@ -1,209 +1,299 @@
 ---
-base_model: gemma3-local
-library_name: peft
+license: mit
+base_model: unsloth/gemma-3-12b-it-qat-bnb-4bit
 tags:
-- base_model:adapter:gemma3-local
-- lora
-- sft
-- transformers
-- trl
+- 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
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
+# Kubernetes AI - Gemma 3 12B LoRA Adapters
 
-### Model Description
+Fine-tuned Gemma 3 12B model specialized for answering Kubernetes questions in Turkish.
 
-<!-- Provide a longer summary of what this model is. -->
+## 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.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Use Cases
 
-### Model Sources [optional]
+- 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**
 
-<!-- Provide the basic links for the model. -->
+## Quick Start
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Installation
 
-## Uses
+```bash
+pip install unsloth
+pip install "transformers>=4.40.0"
+pip install peft
+```
+
+### Loading the Model
+
+```python
+from unsloth import FastLanguageModel
+from peft import PeftModel
+import torch
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# 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
+)
 
-### Direct Use
+# Load Kubernetes AI LoRA adapters
+model = PeftModel.from_pretrained(
+    model,
+    "aciklab/kubernetes-ai-lora"
+)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+# Enable inference mode
+FastLanguageModel.for_inference(model)
 
-[More Information Needed]
+# Example usage (Turkish question)
+messages = [
+    {"role": "user", "content": "Kubernetes'te 3 replikaya sahip bir deployment nasıl oluştururum?"}
+]
 
-### Downstream Use [optional]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to("cuda")
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+outputs = model.generate(
+    input_ids=inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
 
-[More Information Needed]
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
 
-### Out-of-Scope Use
+## Example Questions
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Turkish Examples
 
-[More Information Needed]
+```python
+# Deployment creation
+"Node.js uygulaması için 3 replika, sağlık kontrolleri ve kaynak limitleri olan bir Kubernetes deployment oluştur."
 
-## Bias, Risks, and Limitations
+# Troubleshooting
+"Pod'um CrashLoopBackOff durumunda. Yaygın nedenleri nelerdir ve nasıl debug ederim?"
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+# kubectl commands
+"Production namespace'indeki çalışmayan tüm pod'ları gösteren kubectl komutunu yaz."
 
-[More Information Needed]
+# Best practices
+"Kubernetes'te container güvenliği için en iyi uygulamalar nelerdir?"
 
-### Recommendations
+# Service creation
+"LoadBalancer tipinde bir Kubernetes servisi nasıl yapılandırılır?"
+```
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+### 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
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+- **Base Model**: unsloth/gemma-3-12b-it-qat-bnb-4bit
+- **Method**: LoRA (Low-Rank Adaptation)
+- **Framework**: Unsloth
+- **LoRA Rank**: 16
+- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training Checkpoint**: checkpoint-8175
+- **Max Sequence Length**: 2048 tokens
+- **Training Time**: 28 hours
+- **Hardware**: NVIDIA GeForce RTX 5070 12GB
 
-#### Factors
+## Hardware Requirements
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+- **Minimum VRAM**: 12GB (with 4-bit quantization)
+- **Recommended VRAM**: 24GB (for faster inference)
+- **CPU RAM**: 32GB+
+- **Training Hardware**: RTX 5070 12GB
 
-[More Information Needed]
+## Limitations
 
-#### Metrics
+- Model is specialized for Kubernetes v1.24+ (training data reflects recent versions)
+- 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 are limited
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+## Performance Notes
 
-[More Information Needed]
+- Trained on RTX 5070 12GB in 28 hours
+- Works with 12GB VRAM using 4-bit quantization
+- LoRA adapters are only ~130MB in size
+- Fast startup by loading only adapters without full model reload
 
-### Results
+## License
 
-[More Information Needed]
+This model is released under the **MIT License**. Free to use in commercial and open-source projects.
 
-#### Summary
+## 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
 
-## Model Examination [optional]
+For questions or feedback, please open an issue on the model repository.
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
+---
 
-- PEFT 0.17.1
+**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-turkish-gemma3,
+  author = {aciklab},
+  title = {Kubernetes AI Turkish - Gemma 3 12B LoRA Adapters},
+  year = {2025},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/aciklab/kubernetes-ai-lora},
+  note = {Trained on RTX 5070 12GB in 28 hours}
+}
+```