Simon Willison’s Weblog

2025 Python Packaging Ecosystem Survey. If you make use of Python packaging tools (pip, Anaconda, uv, dozens of others) and have opinions please spend a few minutes with this year's packaging survey. This one was "Co-authored by 30+ of your favorite Python Ecosystem projects, organizations and companies."

# 11:50 am / surveys, packaging, pip, python, psf

qwen2.5vl in Ollama. Ollama announced a complete overhaul of their vision support the other day. Here's the first new model they've shipped since then - a packaged version of Qwen 2.5 VL which was first released on January 26th 2025. Here are my notes from that release.

I upgraded Ollama (it auto-updates so I just had to restart it from the tray icon) and ran this:

ollama pull qwen2.5vl

This downloaded a 6GB model file. I tried it out against my photo of Cleo rolling on the beach:

llm -a https://static.simonwillison.net/static/2025/cleo-sand.jpg \
  'describe this image' -m qwen2.5vl

And got a pretty good result:

The image shows a dog lying on its back on a sandy beach. The dog appears to be a medium to large breed with a dark coat, possibly black or dark brown. It is wearing a red collar or harness around its chest. The dog's legs are spread out, and its belly is exposed, suggesting it might be rolling around or playing in the sand. The sand is light-colored and appears to be dry, with some small footprints and marks visible around the dog. The lighting in the image suggests it is taken during the daytime, with the sun casting a shadow of the dog to the left side of the image. The overall scene gives a relaxed and playful impression, typical of a dog enjoying time outdoors on a beach.

Qwen 2.5 VL has a strong reputation for OCR, so I tried it on my poster:

llm -a https://static.simonwillison.net/static/2025/poster.jpg \
  'convert to markdown' -m qwen2.5vl

The result that came back:

It looks like the image you provided is a jumbled and distorted text, making it difficult to interpret. If you have a specific question or need help with a particular topic, please feel free to ask, and I'll do my best to assist you!

I'm not sure what went wrong here. My best guess is that the maximum resolution the model can handle is too small to make out the text, or maybe Ollama resized the image to the point of illegibility before handing it to the model?

Update: I think this may be a bug relating to URL handling in LLM/llm-ollama. I tried downloading the file first:

wget https://static.simonwillison.net/static/2025/poster.jpg
llm -m qwen2.5vl 'extract text' -a poster.jpg

This time it did a lot better. The results weren't perfect though - it ended up stuck in a loop outputting the same code example dozens of times.

I tried with a different prompt - "extract text" - and it got confused by the three column layout, misread Datasette as "Datasetette" and missed some of the text. Here's that result.

These experiments used qwen2.5vl:7b (6GB) - I expect the results would be better with the larger qwen2.5vl:32b (21GB) and qwen2.5vl:72b (71GB) models.

Fred Jonsson reported a better result using the MLX model via LM studio (~9GB model running in 8bit - I think that's mlx-community/Qwen2.5-VL-7B-Instruct-8bit). His full output is here - looks almost exactly right to me.

# 12:31 pm / vision-llms, llm, ollama, generative-ai, ai, qwen, llms, mlx, ocr

llm-pdf-to-images. Inspired by my previous llm-video-frames plugin, I thought it would be neat to have a plugin for LLM that can take a PDF and turn that into an image-per-page so you can feed PDFs into models that support image inputs but don't yet support PDFs.

This should now do exactly that:

llm install llm-pdf-to-images
llm -f pdf-to-images:path/to/document.pdf 'Summarize this document'

Under the hood it's using the PyMuPDF library. The key code to convert a PDF into images looks like this:

import fitz
doc = fitz.open("input.pdf")
for page in doc:
    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
    jpeg_bytes = pix.tobytes(output="jpg", jpg_quality=30)

Once I'd figured out that code I got o4-mini to write most of the rest of the plugin, using llm-fragments-github to load in the example code from the video plugin:

llm -f github:simonw/llm-video-frames '
import fitz
doc = fitz.open("input.pdf")
for page in doc:
    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
    jpeg_bytes = pix.tobytes(output="jpg", jpg_quality=30)
' -s 'output llm_pdf_to_images.py which adds a pdf-to-images: 
 fragment loader that converts a PDF to frames using fitz like in the example' \
-m o4-mini

Here's the transcript - more details in this issue.

I had some weird results testing this with GPT 4.1 mini. I created a test PDF with two pages - one white, one black - and ran a test prompt like this:

llm -f 'pdf-to-images:blank-pages.pdf' \
  'describe these images'

The first image features a stylized red maple leaf with triangular facets, giving it a geometric appearance. The maple leaf is a well-known symbol associated with Canada.

The second image is a simple black silhouette of a cat sitting and facing to the left. The cat's tail curls around its body. The design is minimalistic and iconic.

I got even wilder hallucinations for other prompts, like "summarize this document" or "describe all figures". I have a collection of those in this Gist.

Thankfully this behavior is limited to GPT-4.1 mini. I upgraded to full GPT-4.1 and got much more sensible results:

llm -f 'pdf-to-images:blank-pages.pdf' \
  'describe these images' -m gpt-4.1

Certainly! Here are the descriptions of the two images you provided:

1. First image: This image is completely white. It appears blank, with no discernible objects, text, or features.

2. Second image: This image is entirely black. Like the first, it is blank and contains no visible objects, text, or distinct elements.

If you have questions or need a specific kind of analysis or modification, please let me know!

# 8:48 pm / llm, plugins, ai, llms, ai-assisted-programming, pdf, generative-ai, projects, hallucinations