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phi3-kubernetes
shlbnrj/phi3-kubernetes
phi3-kubernetes is a machine learning model from shlbnrj. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Phi-3-mini fine-tuned on 300 Kubernetes Q&A samples from Stack Overflow using QLoRA. Built as part of an end-to-end Local Research Assistant pipeline. See the project repo for full context, including dataset preparati…
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At a glance
- License
- mit
- Access
- Public
- Created
- May 19, 2026
- Updated
- May 19, 2026
- SHA
- 81a9f010
Base models
Model card
From the Hugging Face model README
phi3-kubernetes
Phi-3-mini fine-tuned on 300 Kubernetes Q&A samples from Stack Overflow using QLoRA. Built as part of an end-to-end Local Research Assistant pipeline. See the project repo for full context, including dataset preparation, evaluation results, inference server, and CI/CD setup.
Files
| File | Size | Purpose |
|---|---|---|
phi3_kubernetes_lora.zip | 106 MB | LoRA adapter weights (rank=16, alpha=32) |
phi3-kubernetes-q4_k_m.gguf | 2.16 GB | Merged GGUF, 4-bit quant — recommended deployment |
phi3-kubernetes-q8_0.gguf | not hosted | Benchmarked locally; regenerable with llama-quantize from the LoRA adapter |
The Q8_0 quantization was benchmarked during development (see eval section below) but is not hosted here to keep the repo small. The Q4_K_M is the deployment-recommended variant: ~3.7× faster throughput at the same VRAM ceiling.
Training
| Hyperparameter | Value |
|---|---|
| Base model | microsoft/Phi-3-mini-4k-instruct |
| Fine-tune method | QLoRA (Unsloth) |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Learning rate | 2e-4 (cosine schedule) |
| Batch size | 8 (effective 16 via grad accumulation) |
| Epochs | 3 |
| Hardware | Colab T4 (16 GB VRAM) |
| Dataset | mcipriano/stackoverflow-kubernetes-questions (300 samples filtered) |
Evaluation
Tested on 30 held-out K8s Q&A samples against the base phi3:mini model:
| Metric | Base phi3:mini | Fine-tuned phi3-kubernetes | Δ |
|---|---|---|---|
| ROUGE-L | 0.1382 | 0.1622 | +17.4% |
| Avg latency | 18,104 ms | 9,272 ms | 2× faster |
Quantization benchmark (RTX 3050 Laptop, 4 GB VRAM)
| Quant | TTFT (ms) | Throughput (tok/s) | Peak VRAM |
|---|---|---|---|
| Q4_K_M | 2,692 | 63.6 | 3.4 GB |
| Q8_0 | 2,798 | 17.3 | 3.6 GB |
Q4_K_M is ~3.7× faster at the same VRAM ceiling — chosen as the deployment default.
Usage with Ollama
ollama run hf.co/shlbnrj/phi3-kubernetes:Q4_K_M
Or manually:
wget https://huggingface.co/shlbnrj/phi3-kubernetes/resolve/main/phi3-kubernetes-q4_k_m.gguf
cat > Modelfile <<'EOF'
FROM ./phi3-kubernetes-q4_k_m.gguf
PARAMETER temperature 0.4
PARAMETER num_ctx 4096
EOF
ollama create phi3-kubernetes -f Modelfile
ollama run phi3-kubernetes "What is a Pod?"
License
MIT. The base model (Phi-3) is under the Microsoft Research License.
Limitations
This is a small (3.8B parameter) model. It works well for direct K8s knowledge questions but is unreliable for multi-step tool-use scenarios (e.g., chaining a search with a Python computation). See the project's NOTES.md for detailed failure-mode analysis.
- License
- mit
- Languages
- en
- Created
- May 19, 2026
- Updated
- May 19, 2026