Simon Willison’s Weblog

Qwen2.5-VL-32B: Smarter and Lighter. The second big open weight LLM release from China today - the first being DeepSeek v3-0324.

Qwen's previous vision model was Qwen2.5 VL, released in January in 3B, 7B and 72B sizes.

Today's Apache 2.0 licensed release is a 32B model, which is quickly becoming my personal favourite model size - large enough to have GPT-4-class capabilities, but small enough that on my 64GB Mac there's still enough RAM for me to run other memory-hungry applications like Firefox and VS Code.

Qwen claim that the new model (when compared to their previous 2.5 VL family) can "align more closely with human preferences", is better at "mathematical reasoning" and provides "enhanced accuracy and detailed analysis in tasks such as image parsing, content recognition, and visual logic deduction".

They also offer some presumably carefully selected benchmark results showing it out-performing Gemma 3-27B, Mistral Small 3.1 24B and GPT-4o-0513 (there have been two more recent GPT-4o releases since that one, 2024-08-16 and 2024-11-20).

As usual, Prince Canuma had MLX versions of the models live within hours of the release, in 4 bit, 6 bit, 8 bit, and bf16 variants.

I ran the 4bit version (a 18GB model download) using uv and Prince's mlx-vlm like this:

uv run --with 'numpy<2' --with mlx-vlm \
  python -m mlx_vlm.generate \
    --model mlx-community/Qwen2.5-VL-32B-Instruct-4bit \
    --max-tokens 1000 \
    --temperature 0.0 \
    --prompt "Describe this image." \
    --image Mpaboundrycdfw-1.png

Here's the image:

And here's the resulting description, which I found to be very impressive:

This image appears to be a map highlighting specific areas along the coastline, likely in California, based on the geographical features and place names. Here is a detailed description of the image:

1. Geographic Features:

   • The map shows a section of the coastline, with land on the right and the sea on the left.
   • The land area includes parts of SanMateo County, as noted in the top-right corner.
   • The coastline features several named locations, including:
     • Point Montara
     • Pillar Point
     • Kings Rock
     • VicenteCreek
     • DennistonCreek
     • State Beach

2. Highlighted Areas:

   • The map contains two distinct areas marked with different colors:
     • Red Area (Top Section)
       • Labeled as "MONTARA" and "MONTARA M R" (possibly referring to a "Marine Reserves" or similar protected area).
       • This area is marked with a dark red color and extends from the coastline inland.
     • Blue Area (Bottom Section)
       • Labeled as "PILLAR" and "PILLAR P O I N T" along with "SMCA" (Special Management Area).
       • This area is marked with a blue color and is located closer to the coastline, including the area around "Pillar Point."

3. Depth Contours:

   • The map includes depth markings in the sea, indicating the bathymetrical features:
     • 20 fathom (fm) line is marked in the upper-left part of the sea.
     • 10 fathom (fm) line is marked closer to the coastline.
     • 30 fathom (fm) line is marked further out to sea.

4. Other Features:

   • State Beach: Marked near the top-right, indicating a protected recreational area.
   • Kings Rock: A prominent feature near the coastline, likely a rocky outcropping.
   • Creeks: The map shows several creeks, including VicenteCreek and DennistonCreek, which flow into the sea.

5. Protected Areas:

   • The map highlights specific protected areas:
     • Marine Reserves:
       • "MONTARA M R" (Marine Reserves) in red.
     • Special Management Area (SMCA)
       • "PILLAR P O I N T" in blue, indicating a Special Management Area.

6. Grid and Coordinates:

   • The map includes a grid with latitude and longitude markings:
     • Latitude ranges from approximately 37°25'N to 37°35'N.
     • Longitude ranges from approximately 122°22.5'W to 122°35.5'W.

7. Topography:

   • The land area shows topographic features, including elevations and vegetation, with green areas indicating higher elevations or vegetated land.

8. Other Labels:

   • "SMR": Likely stands for "State Managed Reserves."
   • "SMCA": Likely stands for "Special Management Control Area."

In summary, this map highlights specific protected areas along the coastline, including a red "Marine Reserves" area and a blue "Special Management Area" near "Pillar Point." The map also includes depth markings, geographical features, and place names, providing a detailed view of the region's natural and protected areas.

It included the following runtime statistics:

Prompt: 1051 tokens, 111.985 tokens-per-sec
Generation: 760 tokens, 17.328 tokens-per-sec
Peak memory: 21.110 GB

# 24th March 2025, 10:43 pm / vision-llms, llm-release, generative-ai, ai, qwen, llms, uv, mlx, prince-canuma

SmolVLM—small yet mighty Vision Language Model. I've been having fun playing with this new vision model from the Hugging Face team behind SmolLM. They describe it as:

[...] a 2B VLM, SOTA for its memory footprint. SmolVLM is small, fast, memory-efficient, and fully open-source. All model checkpoints, VLM datasets, training recipes and tools are released under the Apache 2.0 license.

I've tried it in a few flavours but my favourite so far is the mlx-vlm approach, via mlx-vlm author Prince Canuma. Here's the uv recipe I'm using to run it:

uv run \
  --with mlx-vlm \
  --with torch \
  python -m mlx_vlm.generate \
    --model mlx-community/SmolVLM-Instruct-bf16 \
    --max-tokens 500 \
    --temp 0.5 \
    --prompt "Describe this image in detail" \
    --image IMG_4414.JPG

If you run into an error using Python 3.13 (torch compatibility) try uv run --python 3.11 instead.

This one-liner installs the necessary dependencies, downloads the model (about 4.2GB, saved to ~/.cache/huggingface/hub/models--mlx-community--SmolVLM-Instruct-bf16) and executes the prompt and displays the result.

I ran that against this Pelican photo:

The model replied:

In the foreground of this photograph, a pelican is perched on a pile of rocks. The pelican’s wings are spread out, and its beak is open. There is a small bird standing on the rocks in front of the pelican. The bird has its head cocked to one side, and it seems to be looking at the pelican. To the left of the pelican is another bird, and behind the pelican are some other birds. The rocks in the background of the image are gray, and they are covered with a variety of textures. The rocks in the background appear to be wet from either rain or sea spray.

There are a few spatial mistakes in that description but the vibes are generally in the right direction.

On my 64GB M2 MacBook pro it read the prompt at 7.831 tokens/second and generated that response at an impressive 74.765 tokens/second.

# 28th November 2024, 8:29 pm / vision-llms, uv, mlx, ai, edge-llms, llms, python, generative-ai, smollm, llm-release, prince-canuma

mlx-vlm (via) The MLX ecosystem of libraries for running machine learning models on Apple Silicon continues to expand. Prince Canuma is actively developing this library for running vision models such as Qwen-2 VL and Pixtral and LLaVA using Python running on a Mac.

I used uv to run it against this image with this shell one-liner:

uv run --with mlx-vlm \
  python -m mlx_vlm.generate \
  --model Qwen/Qwen2-VL-2B-Instruct \
  --max-tokens 1000 \
  --temp 0.0 \
  --image https://static.simonwillison.net/static/2024/django-roadmap.png \
  --prompt "Describe image in detail, include all text"

The --image option works equally well with a URL or a path to a local file on disk.

This first downloaded 4.1GB to my ~/.cache/huggingface/hub/models--Qwen--Qwen2-VL-2B-Instruct folder and then output this result, which starts:

The image is a horizontal timeline chart that represents the release dates of various software versions. The timeline is divided into years from 2023 to 2029, with each year represented by a vertical line. The chart includes a legend at the bottom, which distinguishes between different types of software versions.

Legend

Mainstream Support:

• 4.2 (2023)
• 5.0 (2024)
• 5.1 (2025)
• 5.2 (2026)
• 6.0 (2027) [...]

# 29th September 2024, 9:38 pm / vision-llms, apple, python, generative-ai, uv, ai, llms, mlx, qwen, prince-canuma