chowmean/k8s_Qwen2.5-0.5B-Instruct

🧠 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.