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.
Torq Examples: Use Torq-examples LiquidAI/LiquidAI-LFM2-VL-450M scripts to download and deploy on your Machina kit
SL2600 Machina kit
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)
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).
