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Synaptics/LiquidAI-LFM2-VL-450M

sourceHugging Faceotherupdated 8d agoView on Hugging Face
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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="astrasl2600machina.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

FileSizeSize (W8)Role
vision_encoder_256.vmfb203 MB106 MBSigLIP vision encoder, 256-res → 64 image tokens
decoder_image_2part_A.vmfb353 MB150 MBone-shot image-prefill decoder, layers 0–7
decoder_image_2part_B.vmfb311 MB132 MBone-shot image-prefill decoder, layers 8–15
decoder_nolm.vmfb577 MB290 MBLFM2 single-token decode body (hidden-state output)
lm_head.vmfb134 MB67 MBtied LM head (hidden → 65 536 logits)
token_embeddings.npy134 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

PlatformModel / StageEnvironmentNPU ClockTTFTInfer / s
SL2619 2GBLFM2-VL-450MTorq v2.0.01 GHz2844 ms3.4
SL2619 2GBLFM2-VL-450M-W8Torq v2.0.01 GHz1371 ms6.2

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](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.

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