---
license: other
license_name: lfm-open-license-v1.0
license_link: LICENSE
base_model:
- LiquidAI/LFM2-VL-450M
tags:
- torq
- synaptics
- sl2619
- npu
- edge
- lfm2
- lfm2-vl
pipeline_tag: image-text-to-text
---

# LFM2-VL-450M — Torq build (Synaptics SL2619 NPU)
<img src="synaptics-logo-full-color.png" alt="Synaptics" width="600"/></img>

This repository provides compiled model files for LiquidAI's LFM2-VL-450M vision-language model, ready
to run on the **Synaptics SL2610-series Torq NPU**. 
Give it an image and a natural-language question, and it answers questions about that image.

**Quick start guide**:
- [Buy a Machina kit](https://www.synaptics.com/products/embedded-processors/sl2610-product-line#devKit): Get an SL2600 Machina kit delivered to you
- [Torq Examples](https://synaptics-torq.github.io/torq-compiler/v/latest/): Use Torq-examples LiquidAI/LiquidAI-LFM2-VL-450M scripts to download and deploy on your Machina kit

<img src="astra_sl2600_machina.png" alt="SL2600 Machina kit" width="400"/></img>

## Model Overview

LFM2‑VL is designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications.

LFM2-VL utilizes hybrid conv/attention text decoders that execute on the NPU in bf16; the token
embeddings run on the host CPU.

Image + prompt → caption / visual question answering. The image is encoded once and
its KV cache is reused, so follow-up questions about the same image stay fast.

## Model Features

### Contents

| File | Size | Role |
|---|--:|---|
| `vision_encoder_256.vmfb` | 203 MB | SigLIP vision encoder, 256-res → 64 image tokens |
| `decoder_image_2part_A.vmfb` | 353 MB | one-shot image-prefill decoder, layers 0–7 |
| `decoder_image_2part_B.vmfb` | 311 MB | one-shot image-prefill decoder, layers 8–15 |
| `decoder_nolm.vmfb` | 577 MB | LFM2 single-token decode body (hidden-state output) |
| `lm_head.vmfb` | 134 MB | tied LM head (hidden → 65 536 logits) |
| `token_embeddings.npy` | 134 MB | CPU embedding LUT / tied-LM-head weights (bf16) |
| `config.json`, `tokenizer.json` | — | model config + tokenizer |
| `cats-and-dogs-256.jpg` | — | sample 256-res image for the demo |
| `onnx/` | ~2 GB | reference ONNX exports (vision encoder, merged decoder, embeddings) for non-Torq runtimes |

## Model Details

- **Base model:** LiquidAI LFM2-VL-450M (SigLIP vision tower + LFM2 language model).
- **Text decoder:** LFM2 — hidden size 1024, 16 layers, 16 attention heads, vocabulary 65 536, hybrid short-convolution + grouped-query attention.
- **Image tokens:** 64 per image (256-resolution input).
- **Precision:** bf16 on the NPU.
- **Target:** Synaptics SL2619, compiled with the Torq compiler.
- **On-device performance (SL2619, indicative):** vision encode ~2.4 s, image→KV prefill ~3.7 s, decode ~3.6–4.2 tok/s.

## Tested Platforms

- [Synaptics Astra™ Machina SL2619 2GB](https://www.synaptics.com/products/embedded-processors/astra-machina-foundation-series)
## Metrics

| Platform  | Model / Stage | Environment | NPU Clock | TTFT | Infer / s | 
| --------- | --------- | --------- | --------- | --------- | --------- |
| SL2619 2GB	| LFM2-VL-450M | Torq v2.0.0	| 1 GHz | 2844 ms | 3.4 | 


## Deployment

The models have been tested with the following environment.

- Torq Compiler: *v2.0.0*
- Torq Runtime: *v2.0.0* included in Astra SDK release *scarthgap_6.12_v2.4.0*


### Usage Tutorials / Example Apps

A usage example is provided in the [Torq Examples / LiquidAI-LFM2-VL-450M](https://github.com/synaptics-torq/torq-examples/tree/main/LiquidAI/LiquidAI-LFM2-VL-450M). 

Check out the [README](https://github.com/synaptics-torq/torq-examples/blob/main/LiquidAI/LiquidAI-LFM2-VL-450M/README.md) for instructions.

## License & attribution

This repository is a redistribution of a model created by **Liquid AI, Inc.**, licensed under the **LFM Open License v1.0**. Copies of the license and the attribution notices are included alongside the model files:

- **[LICENSE](https://huggingface.co/Synaptics/LiquidAI-LFM2-VL-450M/blob/main/LICENSE)** — a verbatim copy of the LFM Open License v1.0.
- **[NOTICE](https://huggingface.co/Synaptics/LiquidAI-LFM2-VL-450M/blob/main/NOTICE)** — the copyright, patent, trademark, and attribution
  notices retained from the original Work (per Section 4(c) of the license).

Original model: **[LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2-VL-450M)** · Copyright © Liquid AI, Inc.

## Learn More

- [Synaptics AI Developer Zone](https://developer.synaptics.com?utm_source=hf): Get started with documentation, tutorials and resources for your Edge AI journey.
- [Torq Compiler Documentation](https://synaptics-torq.github.io/torq-compiler/v/latest/): Learn more about the Torq compiler based on MLIR and IREE.
- [Synaptics Astra SDK](https://synaptics-astra.github.io/doc/v/latest/): Learn more about the Yocto Project-based Linux software available for Astra SL processors. 
- [Astra Support Portal](https://synacsm.atlassian.net/servicedesk/customer/portal/543?utm_source=hf): Connect with our engineering team and community.