MrMoeeee/lamp-models

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