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Qwen3 VL 4B Instruct

This repository contains Qwen/Qwen3-VL-4B-Instruct together with a Furiosa Executable Bundle (FXB) for running it on FuriosaAI RNGD with Furiosa-LLM. The same model also runs on other frameworks (such as vLLM,...

Sourcehuggingface.co/furiosa-ai/Qwen3-VL-4B-Instruct/tree/c28008eb5208b5eb87036cfa9dd318bc53f2d2c0
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Qwen/Qwen3-VL-4B-Instruct
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Oct 22, 2026
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Hugging Face

Source description

Qwen3-VL-4B-Instruct

This repository contains Qwen/Qwen3-VL-4B-Instruct together with a Furiosa Executable Bundle (FXB) for running it on FuriosaAI RNGD with Furiosa-LLM. The same model also runs on other frameworks (such as vLLM, SGLang, and Transformers); for usage with those, see the upstream Qwen/Qwen3-VL-4B-Instruct model card.

Overview

Qwen3-VL-4B-Instruct is a 4-billion-parameter dense vision-language model from the Qwen3-VL series. It pairs a vision encoder with a dense transformer decoder, using Interleaved-MRoPE positional embeddings and DeepStack multi-level feature fusion. The upstream model handles images and videos alongside text, covering visual understanding tasks such as OCR, document and chart analysis, spatial reasoning, and video comprehension, and it natively supports tool (function) calling. This is the Instruct (non-thinking) edition. Its intended use is the same as the upstream Qwen/Qwen3-VL-4B-Instruct, released under the Apache 2.0 License.

  • Architecture: Qwen3-VL (dense)
  • Input / Output: Image + Text / Text
  • Supported Inference Engine: Furiosa LLM
  • Supported Hardware: FuriosaAI RNGD

Quantization

No quantization is applied — the model runs in the same precision as the upstream weights.

Features

  • Vision-language. The model accepts OpenAI-style multimodal chat messages with image_url content parts alongside text.
  • Tool calling. The model supports tool (function) calling through the hermes tool-call parser.

Parallelism Strategy

On RNGD, Qwen3-VL-4B-Instruct runs with a tensor-parallel size of 8 PEs, which maps to a single RNGD card (8 PEs per card).

Usage

To run this model with Furiosa-LLM, follow the example commands below after installing Furiosa-LLM and its prerequisites.

Launch the server

The simplest way to serve the model is:

# Launch the server, listening on port 8000 by default
furiosa-llm serve furiosa-ai/Qwen3-VL-4B-Instruct

To also enable tool (function) calling, add the hermes tool-call parser (the parser used by the Qwen3 series):

furiosa-llm serve furiosa-ai/Qwen3-VL-4B-Instruct \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

When the server is ready, you will see:

INFO:     Started server process [27507]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Basic Usage

The server exposes an OpenAI-compatible API. You can send a text-only request with curl:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "furiosa-ai/Qwen3-VL-4B-Instruct",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
    }' \
    | python -m json.tool

To ask about an image, pass an image_url content part in the message:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "furiosa-ai/Qwen3-VL-4B-Instruct",
    "messages": [{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}},
            {"type": "text", "text": "Describe this image."}
        ]
    }]
    }' \
    | python -m json.tool

The image_url.url field accepts a remote http:///https:// URL, an inline base64 data: URL, or a local file:// path (the last requires the --allowed-local-media-path flag described under Advanced Usage).

Advanced Usage

Multimodal serving options. This example allows local images under /srv/media, restricts remote image fetches to cdn.example.com, and limits each request to four images:

furiosa-llm serve furiosa-ai/Qwen3-VL-4B-Instruct \
  --allowed-local-media-path /srv/media \
  --allowed-media-domains cdn.example.com \
  --image-limit-per-prompt 4

See Server Options in the Vision-Language Models guide for the full option reference and defaults.

Image processing options. This example sets a 10-second HTTP image-fetch timeout and uses min_pixels and max_pixels to bound image resizing:

furiosa-llm serve furiosa-ai/Qwen3-VL-4B-Instruct \
  --media-io-kwargs '{"image": {"timeout": 10}}' \
  --mm-processor-kwargs '{"min_pixels": 4096, "max_pixels": 200704}'

For supported values, sizing behavior, and per-request overrides, see Multimodal Options in the Vision-Language Models guide.

Tool calling. With the server launched using --enable-auto-tool-choice --tool-call-parser hermes (see Launch the server), pass tools in the request and let the model decide when to call them. See the Tool Calling guide for a complete client example and details on tool-choice options.

Learn more

Sources

1 source
Hugging FacePrimaryVersion c28008eb5208b5eb87036cfa9dd318bc53f2d2c0
huggingface.co/furiosa-ai/Qwen3-VL-4B-Instruct/tree/c28008eb5208b5eb87036cfa9dd318bc53f2d2c0

Verified Oct 22

Source excerpts

3 excerpts
ReadmeOct 22huggingface.co/furiosa-ai/Qwen3-VL-4B-Instruct

--- base_model: Qwen/Qwen3-VL-4B-Instruct license: apache-2.0 pipeline_tag: image-text-to-text library_name: furiosa-llm tags: - furiosa-ai - qwen3-vl - qwen3_vl --- # Qwen3-VL-4B-Instruct This repository contains [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) together with a Furiosa Executable Bundle (FXB) for running it on [FuriosaAI RNGD](https://furiosa.ai) with [Furiosa-LLM](https://developer.furiosa.ai/latest/en/furiosa_llm/intro.html). The same model also runs on other frameworks (such as vLLM, SGLang, and Transformers); for usage with those, see the upstream [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) model card. ## Overview Qwen3-VL-4B-Instruct is a 4-billion-parameter **dense vision-language model** from the Qwen3-VL series. It pairs a vision encoder with a dense transformer decoder, using Interleaved-MRoPE positional embeddings and DeepStack multi-level feature fusion. The upstream model handles images and videos alongside text, covering visual understanding tasks such as OCR, document and chart analysis, spatial reasoning, and video comprehension, and it natively supports tool (function) calling. This is the **Instruct** (non-thinking) edition. Its intended use is the same as the upstream [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct), released under the Apache 2.0 License. - **Architecture:** Qwen3-VL (dense) - **Input / Output:** Image + Text / Text - **Supported Inference Engine:** Furiosa LLM - **Supported Hardware:** FuriosaAI RNGD ### Quantization No quantization is applied — the model runs in the same precision as the upstream weights. ### Features - **Vision-language.** The model accepts OpenAI-style multimodal chat messages with `image_url` content parts alongside text. - **Tool calling.** The model supports tool (function) calling through the `hermes` tool-call parser. ### Parallelism Strategy On RNGD, Qwen3-VL-4B-Instruct runs with a **tensor-parallel size of 8 PEs**, which maps to a **single RNGD card** (8 PEs per card). ## Usage To run this model with Furiosa-LLM, follow the example commands below after [installing Furiosa-LLM and its prerequisites](https://developer.furiosa.ai/latest/en/get_started/furiosa_llm.html#installing-furiosa-llm). ### Launch the server The simplest way to serve the model is: ```sh # Launch th...

ReadmeJul 17huggingface.co/furiosa-ai/Qwen3-VL-4B-Instruct

--- base_model: Qwen/Qwen3-VL-4B-Instruct license: apache-2.0 pipeline_tag: image-text-to-text library_name: furiosa-llm tags: - furiosa-ai - qwen3-vl - qwen3_vl --- # Qwen3-VL-4B-Instruct This repository contains [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) together with a Furiosa Executable Bundle (FXB) for running it on [FuriosaAI RNGD](https://furiosa.ai) with [Furiosa-LLM](https://developer.furiosa.ai/latest/en/furiosa_llm/intro.html). The same model also runs on other frameworks (such as vLLM, SGLang, and Transformers); for usage with those, see the upstream [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) model card. ## Overview Qwen3-VL-4B-Instruct is a 4-billion-parameter **dense vision-language model** from the Qwen3-VL series. It pairs a vision encoder with a dense transformer decoder, using Interleaved-MRoPE positional embeddings and DeepStack multi-level feature fusion to handle images and videos alongside text. The model covers visual understanding tasks such as OCR, document and chart analysis, spatial reasoning, and video comprehension, and it natively supports tool (function) calling. This is the **Instruct** (non-thinking) edition. Its intended use is the same as the upstream [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct), released under the Apache 2.0 License. - **Architecture:** Qwen3-VL (dense) - **Input / Output:** Image + Text / Text - **Supported Inference Engine:** Furiosa LLM - **Supported Hardware:** FuriosaAI RNGD ### Quantization No quantization is applied — the model runs in the same precision as the upstream weights. ### Features - **Vision-language.** The model accepts OpenAI-style multimodal chat messages with `image_url` content parts alongside text. - **Tool calling.** The model supports tool (function) calling through the `hermes` tool-call parser. ### Parallelism Strategy On RNGD, Qwen3-VL-4B-Instruct runs with a **tensor-parallel size of 8 PEs**, which maps to a **single RNGD card** (8 PEs per card). ## Usage To run this model with Furiosa-LLM, follow the example commands below after [installing Furiosa-LLM and its prerequisites](https://developer.furiosa.ai/latest/en/get_started/furiosa_llm.html#installing-furiosa-llm). ### Launch the server The simplest way to serve the model is: ```sh # Launch the server,...

NoteOct 22huggingface.co/furiosa-ai/Qwen3-VL-4B-Instruct

Source context: 1281 downloads · 1 likes · Pipeline image-text-to-text · Library furiosa-llm · Repo furiosa-ai/Qwen3-VL-4B-Instruct