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-# FastVLM: Efficient Vision Encoding for Vision Language Models
+# FastVLM
-This is the official repository of
-**[FastVLM: Efficient Vision Encoding for Vision Language Models](https://www.arxiv.org/abs/2412.13303). (CVPR 2025)**
+Fast vision-language model architecture research. Part of the Zen LM ecosystem.
-[//]: # ()
-
-
-
+[](https://opensource.org/licenses/Apache-2.0)
-### 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 Zen-7B LLM outperform recent works like Cambrian-1-8B while using a single image encoder with a 7.9x faster TTFT.
-* Demo iOS app to demonstrate the performance of our model on a mobile device.
+## Overview
-
+FastVLM explores efficient architectures for vision-language models, focusing on reducing computational overhead while maintaining strong multimodal understanding.
-## Getting Started
-We use LLaVA codebase to train FastVLM variants. In order to train or finetune your own variants,
-please follow instructions provided in [LLaVA](https://github.com/haotian-liu/LLaVA) codebase.
-We provide instructions for running inference with our models.
+## Features
-### Setup
-```bash
-conda create -n fastvlm python=3.10
-conda activate fastvlm
-pip install -e .
-```
+- Efficient vision-language model architecture
+- Reduced computational overhead vs standard VLMs
+- Strong multimodal understanding
+- Research reference implementation
-### Model Zoo
-For detailed information on various evaluations, please refer to our [paper](https://www.arxiv.org/abs/2412.13303).
+## Related
-| Model | Stage | Pytorch Checkpoint (url) |
-|:-------------|:-----:|:---------------------------------------------------------------------------------------------------------------:|
-| FastVLM-0.5B | 2 | [fastvlm_0.5b_stage2](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_0.5b_stage2.zip) |
-| | 3 | [fastvlm_0.5b_stage3](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_0.5b_stage3.zip) |
-| FastVLM-1.5B | 2 | [fastvlm_1.5b_stage2](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_1.5b_stage2.zip) |
-| | 3 | [fastvlm_1.5b_stage3](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_1.5b_stage3.zip) |
-| FastVLM-7B | 2 | [fastvlm_7b_stage2](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_7b_stage2.zip) |
-| | 3 | [fastvlm_7b_stage3](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_7b_stage3.zip) |
-
-To download all the pretrained checkpoints run the command below (note that this might take some time depending on your connection so might be good to grab ☕️ while you wait).
-
-```bash
-bash get_models.sh # Files will be downloaded to `checkpoints` directory.
-```
-
-### Usage Example
-To run inference of PyTorch checkpoint, follow the instruction below
-```bash
-python predict.py --model-path /path/to/checkpoint-dir \
- --image-file /path/to/image.png \
- --prompt "Describe the image."
-```
-
-### Inference on Apple Silicon
-To run inference on Apple Silicon, pytorch checkpoints have to be exported to format
-suitable for running on Apple Silicon, detailed instructions and code can be found [`model_export`](model_export/) subfolder.
-Please see the README there for more details.
-
-For convenience, we provide 3 models that are in Apple Silicon compatible format: [fastvlm_0.5b_stage3](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_0.5b_stage3_llm.fp16.zip),
-[fastvlm_1.5b_stage3](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_1.5b_stage3_llm.int8.zip),
-[fastvlm_7b_stage3](https://ml-site.cdn-apple.com/datasets/fastvlm/llava-fastvithd_7b_stage3_llm.int4.zip).
-We encourage developers to export the model of their choice with the appropriate quantization levels following
-the instructions in [`model_export`](model_export/).
-
-### Inference on Apple Devices
-To run inference on Apple devices like iPhone, iPad or Mac, see [`app`](app/) subfolder for more details.
-
-## Citation
-If you found this code 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},
-}
-```
-
-## Acknowledgements
-Our codebase is built using multiple opensource contributions, please see [ACKNOWLEDGEMENTS](ACKNOWLEDGEMENTS) for more details.
+- [zen-vl](https://huggingface.co/zenlm/zen-vl-8b-instruct) — Zen vision-language models
+- [jin](https://github.com/zenlm/jin) — Multimodal understanding framework
+- [Zen LM](https://github.com/zenlm) — Full model family
## License
-Please check out the repository [LICENSE](LICENSE) before using the provided code and
-[LICENSE_MODEL](LICENSE_MODEL) for the released models.
+
+See LICENSE file.
+
+*Part of the [Zen LM](https://zenlm.org) ecosystem by [Hanzo AI](https://hanzo.ai)*
