README.md

metadata

base_model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
  - ollama
license: apache-2.0
language:
  - en

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 concise explanations about Kubernetes for general queries.
3. Politely requesting additional information if the instruction is incomplete or ambiguous.

The model was trained efficiently using Unsloth and Hugging Face's TRL library.

How to Use the Model

This section provides instructions on how to run the model using Ollama and the provided Modelfile.

Prerequisites

• Install Ollama on your system.
• Ensure you have access to the model hosted on Hugging Face: hf.co/dereklck/kubernetes_operator_3b_peft_gguf.

Steps

1. Create the Modelfile

   Save the following content as a file named Modelfile:

   FROM hf.co/dereklck/kubernetes_operator_3b_peft_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:
   {{ .Response }}
   </s>
   """
   

2. Create the Model with Ollama

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

   ollama create kubernetes_operator -f Modelfile
   

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

3. Run the Model

   Start interacting with your model:

   ollama run kubernetes_operator
   

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

   Alternatively, you can provide an instruction directly:

   ollama run kubernetes_operator -p "Create a namespace called 'production'."
   

   Example Output:

   kubectl create namespace production
   

---

Model Details

Purpose

The model assists users by:

• Generating accurate kubectl commands based on natural language descriptions.
• Providing brief explanations about Kubernetes for general queries.
• 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: Leveraged 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.

---

Model Features

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 helps users to copy and paste the commands directly into their terminal.

Examples

Example 1: Generating a Command

Instruction:

Create a namespace called 'development'.

Response:

kubectl create namespace development

---

Example 2: Handling Ambiguity

Instruction:

Show me logs.

Response:

Which pod's logs would you like to retrieve?

---

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.

---

Limitations and Considerations

• Accuracy: The model may occasionally produce incorrect or suboptimal commands. Always review the output before execution.
• Hallucinations: In rare cases, the model might generate irrelevant or incorrect information. If the response seems off-topic, consider rephrasing your instruction.
• Security: Be cautious when executing generated commands, especially in production environments.

---

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.

---

Note: This model provides assistance in generating kubectl commands based on user input. Always verify the generated commands in a safe environment before executing them in a production cluster.