Qwen3-VL-4B-Instruct-Autoround-a16w4: Optimized for SiMa.ai Modalix

Overview

This repository contains the Qwen3-VL-4B-Instruct-Autoround-a16w4 model, optimized and compiled for the SiMa.ai Modalix platform.

• Model Architecture: Qwen3-VL (4B parameters)
• Quantization: A16W4 (BF16 activations, INT4 language weights), with mixed-precision exceptions retained from the prequantized checkpoint.
• Pre-quantization: The source checkpoint was pre-quantized with AutoRound before compilation.
• Maximum context length: 4096 tokens
• Input Resolution: 448x448 (Fixed)
• Source Model: Qwen/Qwen3-VL-4B-Instruct

Performance

Text-only generation

The following measurements exercise the language-model component without an image. Vision encoding and projection latency are not included, and input tokens refer only to text tokens.

Measured on SiMa.ai Modalix on 2026-10-27 with MoLE, batch size 1, four samples per input length, a 4,096-token sequence budget, and max_new_tokens=128. Values are arithmetic means. TTFT includes language-model prefill and the first generated token; generation rate is measured after the first token. The ordinary MoLE timing path can emit the first token plus 128 decode steps.

Input tokens	Mean TTFT (seconds)	Mean generation rate (tokens/second)
128	0.14	23.87
256	0.25	23.59
512	0.51	22.89
1024	1.06	21.64
2048	2.46	16.90
3072	4.92	16.03

Image + text generation

Measured on SiMa.ai Modalix on 2026-10-28 (Europe/Berlin), batch size 1, with five requests using one native-resolution dog image and the same text prompt. Chat history and images were cleared between requests. The compiled context budget was 4,096 tokens, including image/text input and generated output.

End-to-end TTFT includes image preprocessing, vision encoding/projection, language-model prefill, and generation of the first token. Generation throughput is the runtime-reported average decode rate after the first token. Table values are arithmetic means of the five CLI measurements.

Image resolution	Mean TTFT (seconds)	Mean generation rate (tokens/second)	Mean generated tokens
448 × 448	0.35	23.31	456

Prompt: What is in this image? Describe the subject, its appearance, surroundings, and important visible details.

These CLI requests generated until EOS or the compiled context limit; they did not use the fixed max_new_tokens=128 setting of the text-only sweep. Output lengths vary across models. These results describe this single-image workload and do not establish general image understanding quality.

Prerequisites

To run this model, you need:

1. SiMa.ai Modalix Device
2. SiMa.ai CLI: Installed on your Modalix device.
3. SiMa.ai Neat Runtime: Install or update the Neat Library on Modalix. The LLiMa runtime is installed as part of the Neat runtime.
4. Hugging Face CLI: Optional, for downloading the model on a host before copying it to Modalix.

Installation & Deployment

Follow these steps to deploy the model to your Modalix device.

1. Install or Update Neat Runtime

Note: This is a one-time setup. If the Neat Library is already installed on your Modalix device, you can skip this step and continue with model download.

Follow the SiMa.ai Neat getting started guide to install or update the Neat Library on your Modalix device.

The llima CLI is available on Modalix after the Neat runtime is installed. It manages precompiled GenAI models under /media/nvme/llima/models by default. Set LLIMA_MODELS_PATH to use a different model directory.

2. Download the Model

Download the compiled model assets from this repository directly to your device.

# Download the model to a local directory
hf download florianvoss/Qwen3-VL-4B-Instruct-Autoround-a16w4 --local-dir /media/nvme/llima/models/Qwen3-VL-4B-Instruct-Autoround-a16w4

Alternatively, you can download the compiled model to a Host and copy it to the Modalix device:

hf download florianvoss/Qwen3-VL-4B-Instruct-Autoround-a16w4 --local-dir Qwen3-VL-4B-Instruct-Autoround-a16w4
scp -r Qwen3-VL-4B-Instruct-Autoround-a16w4 sima@<modalix-ip>:/media/nvme/llima/models/

Replace <modalix-ip> with the IP address of your Modalix device.

Expected Directory Structure:

/media/nvme/llima/
└── models/
    └── Qwen3-VL-4B-Instruct-Autoround-a16w4/   # The compiled model

Usage

Validate with LLiMa CLI

Run the model directly on Modalix:

llima run Qwen3-VL-4B-Instruct-Autoround-a16w4

For all runtime options, run:

llima run -h

GenAI Demo Application

The GenAI demo application is separate from LLiMa installation. Use the GenAI Multimodal Assistant page to install and run the demo app. Once installed, the demo app can use precompiled models such as this one.

API Usage

To serve this model with OpenAI- or Ollama-compatible APIs and send requests to it, use the GenAI server workflow in Serve GenAI Models.

For direct VLM calls without setting up a server, see Run a VLM.

Recompilation

To build for an 8,192-token context, recompile the matching source checkpoint with this repository's compile.sh in a compatible SiMa Model Compiler and LLiMa compilation environment. Change --max_num_tokens 4096 to --max_num_tokens 8192 in the script, then follow its source-checkpoint and output-directory instructions.

The context budget includes input and generated tokens. A larger context increases KV-cache memory requirements and must fit the target device. The downloadable artifacts use a 4,096-token context; any performance measurements in this card refer to that build.

Limitations

• Quantization: This model is quantized (A16W4) for optimal performance on embedded devices. While this maintains high accuracy, minor deviations from the full-precision model may occur.
• Fixed Resolution: While the standard Qwen3-VL architecture supports dynamic input resolutions, this version has been specifically optimized and fixed to 448x448 resolution at compile time to achieve maximum throughput and efficiency on the SiMa.ai MLA.

Troubleshooting

• sima-cli not found: Ensure that sima-cli is installed on your Modalix device.
• llima not found: Install or update the Neat Library. See Getting Started.
• Model can't be run: Verify the model directory is exactly inside /media/nvme/llima/models/ and not nested (e.g., /media/nvme/llima/models/Qwen3-VL-4B-Instruct-Autoround-a16w4/Qwen3-VL-4B-Instruct-Autoround-a16w4).
• Permission Denied: Ensure you have read/write permissions for the /media/nvme directory.

Resources

• GenAI with LLiMa
• Serve GenAI Models
• Run a VLM
• GenAI Multimodal Assistant