metadata
license: apache-2.0
base_model: google/gemma-3-4b-it
tags:
- gemma3
- gguf
- fine-tuned
- lamp
- lighting
- smart-home
- json
datasets:
- custom
pipeline_tag: text-generation
LAMP Models — Fine-tuned for Smart Lighting Control
Fine-tuned language models that generate JSON lighting programs from natural language descriptions.
Models
| Model | Base | Params | GGUF Size | Final Eval Loss |
|---|---|---|---|---|
| lamp-gemma-4b-v2 | Gemma 3 4B IT | 4.3B | ~4.1 GB (Q8_0) | 0.0288 |
Training Details
- Fine-tune Type: Full parameter (no LoRA) — all 4,300,079,472 parameters trained
- Precision: bf16 (bfloat16)
- Dataset: 6,567 training examples + 730 validation examples
- Epochs: 2
- Effective Batch Size: 16 (8 per device × 2 gradient accumulation)
- Learning Rate: 2e-5 with cosine schedule
- Optimizer: AdamW (weight decay 0.01)
- Training Time: 38.1 minutes on NVIDIA H200
- Peak VRAM: 24.3 GB
Training Loss
Training Details
Summary
Usage
With Ollama (GGUF)
# Download the GGUF file and Modelfile from lamp-gemma-4b-v2-gguf/
ollama create lamp-gemma -f Modelfile
ollama run lamp-gemma "warm and cozy lighting"
With Transformers (HuggingFace)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("MrMoeeee/lamp-models", subfolder="lamp-gemma-4b-v2")
tokenizer = AutoTokenizer.from_pretrained("MrMoeeee/lamp-models", subfolder="lamp-gemma-4b-v2")
Files
lamp-gemma-4b-v2/ # Full model weights + training logs
├── model-00001-of-00002.safetensors
├── model-00002-of-00002.safetensors
├── config.json
├── tokenizer.json
├── training_config.json
├── training_log.json
├── training_metrics.csv
├── metrics_detailed.json
└── graphs/
├── training_loss.png
├── training_details.png
└── training_summary.png
lamp-gemma-4b-v2-gguf/ # Quantized GGUF for inference
├── lamp-gemma-4b-v2-Q8_0.gguf
└── Modelfile
Dataset
The LAMP dataset consists of natural language lighting requests paired with JSON lighting programs. Each program controls RGB LEDs with support for:
- Static colors and gradients
- Animations (breathing, rainbow, chase, etc.)
- Multi-step sequences with timing
- Brightness and speed control