Instructions to use chowmean/k8s_Qwen2.5-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="chowmean/k8s_Qwen2.5-0.5B-Instruct",
	filename="k8s.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chowmean/k8s_Qwen2.5-0.5B-Instruct
# Run inference directly in the terminal:
llama cli -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chowmean/k8s_Qwen2.5-0.5B-Instruct
# Run inference directly in the terminal:
llama cli -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf chowmean/k8s_Qwen2.5-0.5B-Instruct
# Run inference directly in the terminal:
./llama-cli -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf chowmean/k8s_Qwen2.5-0.5B-Instruct
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Use Docker

docker model run hf.co/chowmean/k8s_Qwen2.5-0.5B-Instruct

LM Studio
Jan

vLLM

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chowmean/k8s_Qwen2.5-0.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chowmean/k8s_Qwen2.5-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chowmean/k8s_Qwen2.5-0.5B-Instruct

Ollama
How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with Ollama:
```
ollama run hf.co/chowmean/k8s_Qwen2.5-0.5B-Instruct
```

Unsloth Studio

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for chowmean/k8s_Qwen2.5-0.5B-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for chowmean/k8s_Qwen2.5-0.5B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chowmean/k8s_Qwen2.5-0.5B-Instruct to start chatting

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "chowmean/k8s_Qwen2.5-0.5B-Instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default chowmean/k8s_Qwen2.5-0.5B-Instruct

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chowmean/k8s_Qwen2.5-0.5B-Instruct

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "chowmean/k8s_Qwen2.5-0.5B-Instruct" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/chowmean/k8s_Qwen2.5-0.5B-Instruct
```

Lemonade

How to use chowmean/k8s_Qwen2.5-0.5B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chowmean/k8s_Qwen2.5-0.5B-Instruct

Run and chat with the model

lemonade run user.k8s_Qwen2.5-0.5B-Instruct-{{QUANT_TAG}}

List all available models

lemonade list

🧠 k8s_Qwen2.5-0.5B-Instruct

k8s_Qwen2.5-0.5B-Instruct is a domain-specific instruction-tuned language model optimized for Kubernetes (k8s) use cases such as command generation, explanations, and YAML assistance.

The model is fine-tuned from Qwen2.5-0.5B-Instruct on Kubernetes-focused data to improve accuracy and relevance for DevOps and platform engineering workflows.

📌 Key Features

🧾 Generate kubectl commands from natural language

📖 Explain Kubernetes concepts and commands

🛠 Assist with Kubernetes resource creation & troubleshooting

⚡ Lightweight (0.5B parameters) – fast and efficient

💻 Suitable for local inference and fine-tuning

🧬 Model Details

Attribute Value Base Model Qwen2.5-0.5B-Instruct Parameters ~0.5 Billion Architecture Transformer Fine-tuning Domain Kubernetes / DevOps Task Type Instruction Following License Apache-2.0 Framework Hugging Face Transformers

🚀 Quick Start

Installation

pip install transformers torch

Inference Example (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "chowmean/k8s_Qwen2.5-0.5B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

prompt = "Create a Kubernetes deployment named nginx with 3 replicas."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🎯 Intended Use Cases

DevOps automation assistants

Kubernetes learning & documentation tools

CLI copilots

Chatbots for platform engineering teams

Lightweight local LLM experimentation

⚠️ Limitations

Not designed for large-scale reasoning or general knowledge tasks

May hallucinate commands—always validate before production use

Optimized for Kubernetes domain only

📖 Citation

If you use this model, please cite the original Qwen2.5 work:

@misc{qwen2.5, title={Qwen2.5: A Party of Foundation Models}, author={Qwen Team}, year={2024}, url={https://qwenlm.github.io/blog/qwen2.5/} }

🤝 Acknowledgements

Qwen Team for the base Qwen2.5 models

Hugging Face for the open ML ecosystem

📬 Feedback & Contributions

Issues, suggestions, and improvements are welcome via Hugging Face discussions.

Downloads last month: 4

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for chowmean/k8s_Qwen2.5-0.5B-Instruct

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(244)

this model

chowmean
/

k8s_Qwen2.5-0.5B-Instruct

🧠 k8s_Qwen2.5-0.5B-Instruct

📌 Key Features

🧬 Model Details

🚀 Quick Start

Installation

🎯 Intended Use Cases

⚠️ Limitations

📖 Citation

🤝 Acknowledgements

📬 Feedback & Contributions

Model tree for chowmean/k8s_Qwen2.5-0.5B-Instruct

Dataset used to train chowmean/k8s_Qwen2.5-0.5B-Instruct