Update README.md

README.md

@@ -16,17 +16,31 @@ 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>
 
-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
+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.
 
-## Contents
+## Model Features
+
+### Contents
 
 | File | Size | Role |
 |---|--:|---|
@@ -40,28 +54,7 @@ its KV cache is reused, so follow-up questions about the same image stay fast.
 | `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-LFM2-VL-450M** demo in
-[synaptics-torq/torq-examples](https://github.com/synaptics-torq/torq-examples/tree/main/LiquidAI/LiquidAI-LFM2-VL-450M):
-
-```sh
-# downloads this repo to models/Synaptics/LiquidAI-LFM2-VL-450M/
-python setup_demos.py LiquidAI-LFM2-VL-450M
-
-cd LiquidAI/LiquidAI-LFM2-VL-450M
-MODELS=../../models/Synaptics/LiquidAI-LFM2-VL-450M
-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
+## 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.
@@ -70,14 +63,43 @@ Then ask questions at the `Q:` prompt (e.g. *"What is the breed of the dog?"*).
 - **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:
+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-VL-450M](https://huggingface.co/LiquidAI/LFM2-VL-450M)** · Copyright © Liquid AI, Inc.
+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.