README.md

---
license: gemma
license_link: https://ai.google.dev/gemma/terms
base_model: google/gemma-3-4b-pt
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - vision-language
  - multimodal
  - gemma3
  - home-security
  - smart-home
  - on-device
language:
  - en
extra_gated_heading: Access Glimpse-v1
extra_gated_description: >-
  Glimpse-v1 is a Model Derivative of Google's Gemma and is distributed under
  the Gemma Terms of Use. By requesting access you agree to those terms,
  including the Gemma Prohibited Use Policy.
extra_gated_button_content: Acknowledge and access
---
<p align="center">
<picture>
  <img alt="LLM Vision Logo" src="https://github.com/valentinfrlch/ha-llmvision/raw/main/logos/dark_logo@2x.png" width="512">
</picture>
  <h1 align="center">Glimpse-v1</h1>
  <p align="center">A lightweight, open vision-language model built to understand and summarize <b>home security camera events</b>.</p>
</p>

```
ollama run llmvision/glimpse-v1
```

> Install [Ollama](https://ollama.com), then paste the command above. See the [project site](https://llmvision.org/glimpse/) for documentation.

## Model summary

| | |
|---|---|
| **Developer** | LLM Vision |
| **Base model** | `google/gemma-3-4b-pt` |
| **Architecture** | Gemma 3 (vision-language) |
| **Parameters** | ~4B |
| **Modality** | Image + text → text |
| **Training samples** | 5,000+ real-world home security camera events |
| **Reported gain** | 1.9× accuracy improvement over the base model on the target task |
| **License** | [Gemma Terms of Use](https://ai.google.dev/gemma/terms) |

## Intended use

Glimpse-v1 is purpose-built for **summarizing and describing footage from home security cameras** — for example, generating short natural-language descriptions of motion events, deliveries, visitors, pets, or vehicles, locally on consumer hardware.

### Designed for
- Local, privacy-preserving smart-home automations using LLM Vision
- Event summaries for camera notifications
- Edge devices and machines with limited VRAM/RAM

### Not designed for
- General-purpose visual question answering or document understanding
- Person identification, biometric recognition, or surveillance of identifiable individuals
- Safety-critical decisions (medical, legal, security response) without human review
- Use cases prohibited by the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy)

## Languages

English only for now.

## Why a small model?

Glimpse-v1 is a **compact 4B-parameter** model deliberately sized to run on hardware with limited memory and compute. The goal is **private, local AI for the home**: your camera footage never has to leave your network, and you avoid recurring API costs.

## Performance

Glimpse-v1 reports a **1.9× accuracy improvement** over the base Gemma 3 4B model on home-security event summarization. See the [project site](https://llmvision.org/glimpse/) for the latest benchmarks.

## Training

- **Base:** Gemma 3 4B
- **Data:** ~5,000 curated real-world home security camera events spanning diverse scenes, lighting conditions, and event types
- **Objective:** Supervised fine-tuning for concise, factual event descriptions

> Files in this repository have been **modified from the original Gemma 3 release** as part of this fine-tune.

## How to use

### Ollama (recommended)
```

ollama run llmvision/glimpse-v1

```

### Transformers
```
from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
model_id = "llmvision/glimpse-v1"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [
{"role": "user", "content": [
{"type": "image", "url": "path/to/frame.jpg"},
{"type": "text", "text": "Summarize this camera event in one sentence."},
]},
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
).to(model.device)
out = model.generate(inputs, max_new_tokens=128)
print(processor.decode(out[0], skip_special_tokens=True))
```

## Limitations and risks

- **Domain-specific.** Outside of home-security framing, quality drops noticeably.
- **Hallucination.** Like all VLMs, it can invent details (people, objects, actions) not present in the image. Treat outputs as suggestions, not ground truth.
- **Bias.** Training data reflects the distribution of available home camera footage and may underperform on under-represented scenes, lighting, or demographics.
- **Privacy.** Although the model runs locally, **you** are responsible for handling footage of identifiable people in line with local laws (e.g. GDPR).
- **Not a security system.** Do not use Glimpse-v1 as the sole signal for emergency response.

## License

This model is a **Gemma Model Derivative** and is distributed under the [**Gemma Terms of Use**](https://ai.google.dev/gemma/terms). Use, reproduction, modification, and redistribution are subject to those terms, including the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).

By downloading or using Glimpse-v1 you agree to the Gemma Terms of Use. If you redistribute Glimpse-v1 or any derivative of it, you must:
1. Pass these terms through to your recipients as an enforceable provision.
2. Provide recipients a copy of the Gemma Terms of Use.
3. Mark any modified files with prominent notices that they have been modified.
4. Include a `NOTICE` file containing:
   > Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.

## Citation

```
@misc{glimpse_v1,
title  = {Glimpse-v1: A compact vision-language model for home security event understanding},
author = {Valentin Fröhlich},
year   = {2026},
url    = {https://llmvision.org/glimpse/}
}

```

## Acknowledgements

Built on [Google Gemma 3](https://ai.google.dev/gemma). Distributed via [Ollama](https://ollama.com) and Hugging Face.