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metadata

base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
  - ollama
license: apache-2.0
language:
  - en

kubernetes Operator Model - 3B

Developed by: dereklck

License: Apache-2.0

Fine-tuned from model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit

Model Type: GGUF (Quantized format compatible with Ollama)

Language: English

This Llama-based model has been fine-tuned to generate kubectl commands based on user descriptions. Leveraging the capabilities of a 3B parameter model, it offers enhanced understanding and generation abilities compared to its 1B counterpart. The model has been trained efficiently using the Unsloth framework and Hugging Face's TRL library.

📌 Model Details

🚀 Purpose

The model assists users by:

Generating accurate kubectl commands based on natural language descriptions.
Providing concise explanations about Kubernetes for general queries.
Politely requesting additional information when the instruction is incomplete or ambiguous.

👥 Intended Users

Kubernetes Administrators
DevOps Engineers
Developers Working with Kubernetes Clusters

🛠️ Training Process

Base Model: Unsloth's Llama-3.2-3B-Instruct-bnb-4bit
Fine-tuning: Utilized the Unsloth framework and Hugging Face's TRL library for efficient and effective training.
Training Data: Customized dataset focused on Kubernetes operations and kubectl command usage, containing approximately 1,500 entries for improved coverage.

✨ Key Features

1️⃣ Command Generation

Functionality: Translates user instructions into executable kubectl commands.
Example:

User: "Create a namespace called 'production'."

Model Response:
```
kubectl create namespace production
```

2️⃣ Clarification Requests

Functionality: Politely asks for more details when the instruction is incomplete.
Example:

User: "Show me logs."

Model Response:

"Please specify the name of the pod for which you want to view logs."

3️⃣ Knowledge Base

Functionality: Provides concise and accurate explanations for general Kubernetes concepts.
Example:

User: "What is a Deployment in Kubernetes?"

Model Response:

"A Deployment is a Kubernetes resource that manages a replicated application, ensuring that the desired number of pod replicas are running."

4️⃣ Input Sensitivity

Functionality: Recognizes inappropriate language and maintains professionalism.
Example:

User: "What the heck is wrong with this cluster?"

Model Response:

"Could you please provide more details about the issue you're experiencing with your cluster?"

💻 Usage Instructions

📝 Prompt Template

The model uses the following prompt template to generate responses:

You are an AI assistant that helps users with Kubernetes commands and questions.

**Guidelines:**

1. **For clear instructions:**
   - Provide **only** the exact `kubectl` command needed.
   - Do **not** include explanations, placeholders, or extra context.
   - Enclose the command within a code block using `bash` syntax.

2. **For incomplete or ambiguous instructions:**
   - **Politely ask** the user for specific missing information.
   - Do **not** provide any commands or placeholders.
   - Respond in plain text, clearly stating what information is needed.

3. **For general Kubernetes questions:**
   - Provide a **concise and accurate explanation**.
   - Do **not** include any commands unless specifically requested.

**Important Rules:**

- Do **not** generate commands with placeholders or example names like `<pod_name>`.
- Ensure all commands are **valid and executable** as provided.
- If unsure due to unclear instructions, **ask for clarification** without including any commands.
- Do **not** add extra text beyond what's necessary.

---

### Instruction:
{instruction}

### Response:

⚙️ Parameters

Temperature: 0.3 (Controls the randomness; lower values make outputs more deterministic.)
Stop Token: </s> (Indicates the end of the assistant's response.)

🔍 Model Performance

Enhanced Understanding

With 3 billion parameters, this model provides:

Improved comprehension of user instructions, even when phrased in complex ways.
Better handling of edge cases and less common Kubernetes operations.

Consistent Professionalism

Maintains a professional and courteous tone in all interactions.
Filters inappropriate language and focuses on providing assistance.

Examples of Reactions to Questions

📗 Generating Complex Commands

User: "Scale deployment 'backend-service' to 5 replicas."

Model Response:

kubectl scale deployment backend-service --replicas=5

❓ Handling Ambiguity

User: "Delete the resource."

Model Response:

"Please specify the type and name of the resource you wish to delete."

🧠 Explaining Concepts

User: "Explain how Kubernetes handles Secrets."

Model Response:

"Kubernetes Secrets allow you to store and manage sensitive information, such as passwords and tokens, in a secure manner. They can be mounted as files or exposed as environment variables in pods."

🛑 Addressing Inappropriate Language

User: "What the heck is wrong with this cluster?"

Model Response:

"Could you please provide more details about the issue you're experiencing with your cluster?"

🚀 Deployment with Ollama

Prerequisites

Install Ollama on your system.

Download the GGUF model directly from Hugging Face:

ollama pull hf.co/dereklck/kubectl_operator_3b_peft_gguf

Steps to Use the Model

Run the Model

ollama run dereklck/kubectl_operator_3b_peft_gguf

Interact with the Model

Provide your instruction when prompted by the model.

⚠️ Limitations and Considerations

Accuracy

While the model aims to generate precise commands, always review the output before execution to ensure it's appropriate for your environment.

Handling of Uncommon Scenarios

For highly specialized or unusual Kubernetes commands, the model may not provide the desired output.

Security

Be cautious when executing commands that make changes to your cluster. Backup important data and test commands in a safe environment when possible.

🤝 Feedback and Contributions

We welcome feedback and contributions to improve the model and dataset. If you encounter issues or have suggestions:

GitHub: Unsloth Repository
Contact: Reach out to dereklck for assistance or collaboration opportunities.

📄 License

This model is released under the Apache-2.0 License.

🏁 Conclusion

The kubectl Operator Model - 3B GGUF offers a powerful tool for Kubernetes practitioners, providing reliable command generation and valuable explanations. Its enhanced capabilities make it a valuable asset for managing Kubernetes clusters more efficiently.

Note: This model card provides comprehensive information about the kubectl Operator Model - 3B GGUF, highlighting its features and guiding you on how to deploy and interact with the model effectively.

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Verification:

base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
  - ollama
license: apache-2.0
language:
  - en