---
license: other
license_name: lfm-open-license-v1.0
license_link: https://huggingface.co/LiquidAI/LFM2-VL-450M
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)

Pre-compiled **Torq VMFB** build of LiquidAI's
[LFM2-VL-450M](https://huggingface.co/LiquidAI/LFM2-VL-450M) vision-language model,
ready to run on the Synaptics **SL2619** edge NPU. The SigLIP vision encoder and the
LFM2 hybrid conv/attention text decoder both 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.

## 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 |

## Quick start

Runs through the **liquidAI-VLM** demo in
[synaptics-torq/torq-examples](https://github.com/synaptics-torq/torq-examples):

```sh
# downloads this repo to models/Synaptics/liquidAI-LFM2-VLM/
python setup_demos.py liquidAI-VLM

cd liquidAI-VLM
MODELS=../models/Synaptics/liquidAI-LFM2-VLM
python src/infer.py \
  -m              $MODELS/decoder_nolm.vmfb \
  --lm-head       $MODELS/lm_head.vmfb \
  --vision        $MODELS/vision_encoder_256.vmfb \
  --image-decoder $MODELS/decoder_image_2part_ \
  --image         $MODELS/cats-and-dogs-256.jpg
```

Then ask questions at the `Q:` prompt (e.g. *"What is the breed of the dog?"*).

## 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.

## License

Derived from LiquidAI's LFM2-VL-450M and distributed under LiquidAI's **LFM Open
License v1.0** — see the [base model](https://huggingface.co/LiquidAI/LFM2-VL-450M)
for the full terms.