dereklck/kubernetes_operator_1b_peft_gguf

kubectl Operator Model

• Developed by: dereklck
• License: Apache-2.0
• Fine-tuned from model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit
• Model type: GGUF (compatible with Ollama)
• Language: English

This Llama-based model was fine-tuned to assist users with Kubernetes commands and questions. It has three primary features:

1. Generating accurate kubectl commands based on user descriptions.
2. Providing brief explanations about Kubernetes for general queries.
3. Politely requesting additional information if the instruction is incomplete or ambiguous.

Important Note: The 1B model may sometimes generate non-executable CLI commands containing placeholders like <pod_name> or <resource_name>. This can lead to hallucinations and commands that cannot be executed directly. Users are advised to replace these placeholders with actual values before execution. This issue has been significantly improved in the 3B model, which generates more accurate and executable commands without unnecessary placeholders.

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Model Details

Purpose

The model assists users by:

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

Intended Users

• Kubernetes administrators
• DevOps engineers
• Developers working with Kubernetes clusters

Training Process

• Base Model: Unsloth's Llama-3.2-1B-Instruct-bnb-4bit
• Fine-tuning: Utilized the Unsloth framework and Hugging Face's TRL library for efficient training.
• Training Data: Customized dataset focused on Kubernetes operations and kubectl command usage, containing approximately 200 entries.

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Model Features

1. Command Generation in Bash Format

When the model generates CLI commands, it provides them in bash format, enclosed within code blocks for easy execution and clarity. This allows users to copy and paste the commands directly into their terminal.

2. Handling Ambiguity with Polite Clarifications

If the instruction is incomplete or ambiguous, the model politely asks for the specific missing information instead of making assumptions. This ensures accuracy and prevents the execution of incorrect commands.

3. Providing Concise Explanations

For general Kubernetes questions, the model offers concise and accurate explanations without unnecessary details, helping users understand concepts quickly.

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Usage

Parameters

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

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Examples

Example 1: Generating a Command

Instruction:

Create a namespace called 'development'.

Response:

kubectl create namespace development

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Example 2: Handling Ambiguity

Instruction:

Show me logs.

Response:

Which pod's logs would you like to retrieve?

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Example 3: Providing Explanations

Instruction:

What is a Deployment in Kubernetes?

Response:

A Deployment provides declarative updates for Pods and ReplicaSets, allowing you to manage your applications by defining the desired state and letting Kubernetes handle the rest.

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Limitations and Considerations

• Accuracy: The model may occasionally produce incorrect or suboptimal commands. Always review the output before execution.

• Hallucinations: The 1B model may generate CLI commands with placeholders (e.g., <pod_name>, <resource_name>), resulting in commands that are not directly executable. Users should carefully replace these placeholders with actual values.

  • Improvement in 3B Model: This issue has been addressed in the 3B model, where the generation of non-executable commands with placeholders has been significantly reduced, providing more accurate and usable outputs.

• Security: Be cautious when executing generated commands, especially in production environments.

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Future Revisions

• Addressing Hallucinations: Efforts are underway to further reduce hallucinations in future versions, building upon improvements made in the 3B model.
• Enhanced Dataset: Expanding the training dataset to include a wider range of Kubernetes operations to improve the model's versatility.
• Fine-tuning Techniques: Implementing advanced fine-tuning methods to enhance accuracy and reliability.

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Deployment with Ollama

Prerequisites

• Install Ollama on your system.
• Ensure you have the GGUF model file (e.g., kubectl_operator.Q8_0.gguf).

Steps

1. Create the Modelfile

   Save the following content as a file named Modelfile:

   FROM kubectl_operator.Q8_0.gguf
   
   PARAMETER temperature 0.3
   PARAMETER stop "</s>"
   
   TEMPLATE """
   You are an AI assistant that helps users with Kubernetes commands and questions.
   
   **Your Behavior Guidelines:**
   
   1. **For clear and complete instructions:**
      - **Provide only** the exact `kubectl` command needed to fulfill the user's request.
      - Do not include extra explanations, placeholders, or context.
      - **Enclose the command within a code block** with `bash` syntax highlighting.
   
   2. **For incomplete or ambiguous instructions:**
      - **Politely ask** the user for the specific missing information.
      - Do **not** provide any commands or placeholders in your response.
      - 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.
      - Ensure that the explanation fully addresses the user's question.
   
   **Important Rules:**
   
   - **Do not generate CLI commands containing placeholders** (e.g., `<pod_name>`, `<resource_name>`).
   - Ensure all CLI commands are **complete, valid, and executable** as provided.
   - If user input is insufficient to form a complete command, **ask for clarification** instead of using placeholders.
   - Provide only the necessary CLI command output without any additional text.
   
   ### Instruction:
   {{ .Prompt }}
   
   ### Response:
   """
   

2. Create the Model with Ollama

   Open your terminal and run the following command to create the model:

   ollama create kubectl_operator -f Modelfile
   

   This command tells Ollama to create a new model named kubectl_operator using the configuration specified in Modelfile.

3. Run the Model

   Start interacting with your model:

   ollama run kubectl_operator
   

   This will initiate the model and prompt you for input based on the template provided.

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Feedback and Contributions

We welcome any comments or participation to improve the model and dataset. If you encounter issues or have suggestions for improvement:

• GitHub: Unsloth Repository
• Contact: Reach out to the developer, dereklck, for further assistance.

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Note: This model assists in generating kubectl commands based on user input. Always verify the generated commands and replace any placeholders with actual values before executing them in a production cluster.

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