kubernetes_operator_3b_peft_gguf / README.md

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	---
	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](https://huggingface.co/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](https://github.com/unslothai/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](https://github.com/jmorganca/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`:

	```plaintext
	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:

	```bash
	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:

	```bash
	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:

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

	Example Output:

	```bash
	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:

	```bash
	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](https://github.com/unslothai/unsloth)
	- 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.