# FastVLM-1.5B-int8

**Repository Path**: hf-models/FastVLM-1.5B-int8

## Basic Information

- **Project Name**: FastVLM-1.5B-int8
- **Description**: Mirror of https://huggingface.co/apple/FastVLM-1.5B-int8
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-30
- **Last Updated**: 2025-08-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

---
license: apple-amlr
license_name: apple-ascl
license_link: https://github.com/apple/ml-fastvlm/blob/main/LICENSE_MODEL
library_name: ml-fastvlm
---
# FastVLM: Efficient Vision Encoding for Vision Language Models

FastVLM was introduced in
**[FastVLM: Efficient Vision Encoding for Vision Language Models](https://www.arxiv.org/abs/2412.13303). (CVPR 2025)**

[//]: # (![FastViTHD Performance](acc_vs_latency_qwen-2.png))
<p align="center">
<img src="acc_vs_latency_qwen-2.png" alt="Accuracy vs latency figure." width="400"/>
</p>

### Highlights
* We introduce FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images.  
* Our smallest variant outperforms LLaVA-OneVision-0.5B with 85x faster Time-to-First-Token (TTFT) and 3.4x smaller vision encoder.
* Our larger variants using Qwen2-7B LLM outperform recent works like Cambrian-1-8B while using a single image encoder with a 7.9x faster TTFT.


### Evaluations
| Benchmark     | FastVLM-0.5B | FastVLM-1.5B | FastVLM-7B |
|:--------------|:------------:|:------------:|:----------:|
| Ai2D          |     68.0     |     77.4     |    83.6    |
| ScienceQA     |     85.2     |     94.4     |    96.7    |
| MMMU          |     33.9     |     37.8     |    45.4    |
| VQAv2         |     76.3     |     79.1     |    80.8    |
| ChartQA       |     76.0     |     80.1     |    85.0    |
| TextVQA       |     64.5     |     70.4     |    74.9    |
| InfoVQA       |     46.4     |     59.7     |    75.8    |
| DocVQA        |     82.5     |     88.3     |    93.2    |
| OCRBench      |     63.9     |     70.2     |    73.1    |
| RealWorldQA   |     56.1     |     61.2     |    67.2    |
| SeedBench-Img |     71.0     |     74.2     |    75.4    |


### Usage Example
The model has been exported to run with MLX. Follow the instructions in the official repository to use it in an iOS or macOS app.


## Citation
If you found this model useful, please cite the following paper:
```
@InProceedings{fastvlm2025,
  author = {Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari},
  title = {FastVLM: Efficient Vision Encoding for Vision Language Models},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2025},
}
```