---
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 Loss](lamp-gemma-4b-v2/graphs/training_loss.png)

## Training Details

![Training Details](lamp-gemma-4b-v2/graphs/training_details.png)

## Summary

![Training Summary](lamp-gemma-4b-v2/graphs/training_summary.png)

## Usage

### With Ollama (GGUF)

```bash
# 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)

```python
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