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LiquidAI-LFM2-VL-450M
Synaptics/LiquidAI-LFM2-VL-450M
LiquidAI-LFM2-VL-450M is a image-text-to-text model from Synaptics. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. The card lists the license as other.
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At a glance
- Task
- Image-Text-to-Text
- License
- other
- Model type
- lfm2
- Access
- Public
- Created
- Jul 6, 2026
- Updated
- Oct 3, 2026
- SHA
- f92ee907
Try a prompt
Base models
Model card
From the Hugging Face model README
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: Get an SL2600 Machina kit delivered to you
- Torq Examples: 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 | Size (W8) | Role |
|---|---|---|---|
vision_encoder_256.vmfb | 203 MB | 106 MB | SigLIP vision encoder, 256-res → 64 image tokens |
decoder_image_2part_A.vmfb | 353 MB | 150 MB | one-shot image-prefill decoder, layers 0–7 |
decoder_image_2part_B.vmfb | 311 MB | 132 MB | one-shot image-prefill decoder, layers 8–15 |
decoder_nolm.vmfb | 577 MB | 290 MB | LFM2 single-token decode body (hidden-state output) |
lm_head.vmfb | 134 MB | 67 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
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 |
| SL2619 2GB | LFM2-VL-450M-W8 | Torq v2.0.0 | 1 GHz | 1371 ms | 6.2 |
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.
Check out the README 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 — a verbatim copy of the LFM Open License v1.0.
- 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 · Copyright © Liquid AI, Inc.
Learn More
- Synaptics AI Developer Zone: Get started with documentation, tutorials and resources for your Edge AI journey.
- Torq Compiler Documentation: Learn more about the Torq compiler based on MLIR and IREE.
- Synaptics Astra SDK: Learn more about the Yocto Project-based Linux software available for Astra SL processors.
- Astra Support Portal: Connect with our engineering team and community.
- Task
- Image-Text-to-Text
- Type
- lfm2
- License
- other
- Created
- Jul 6, 2026
- Updated
- Oct 3, 2026