Simon Willison’s Weblog

Mistral Small 3.1. Mistral Small 3 came out in January and was a notable, genuinely excellent local model that used an Apache 2.0 license.

Mistral Small 3.1 offers a significant improvement: it's multi-modal (images) and has an increased 128,000 token context length, while still "fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized" (according to their model card). Mistral's own benchmarks show it outperforming Gemma 3 and GPT-4o Mini, but I haven't seen confirmation from external benchmarks.

Despite their mention of a 32GB MacBook I haven't actually seen any quantized GGUF or MLX releases yet, which is a little surprising since they partnered with Ollama on launch day for their previous Mistral Small 3. I expect we'll see various quantized models released by the community shortly.

Update 20th March 2025: I've now run the text version on my laptop using mlx-community/Mistral-Small-3.1-Text-24B-Instruct-2503-8bit and llm-mlx:

llm mlx download-model mlx-community/Mistral-Small-3.1-Text-24B-Instruct-2503-8bit -a mistral-small-3.1
llm chat -m mistral-small-3.1

The model can be accessed via Mistral's La Plateforme API, which means you can use it via my llm-mistral plugin.

Here's the model describing my photo of two pelicans in flight:

llm install llm-mistral
# Run this if you have previously installed the plugin:
llm mistral refresh
llm -m mistral/mistral-small-2503 'describe' \
  -a https://static.simonwillison.net/static/2025/two-pelicans.jpg

The image depicts two brown pelicans in flight against a clear blue sky. Pelicans are large water birds known for their long bills and large throat pouches, which they use for catching fish. The birds in the image have long, pointed wings and are soaring gracefully. Their bodies are streamlined, and their heads and necks are elongated. The pelicans appear to be in mid-flight, possibly gliding or searching for food. The clear blue sky in the background provides a stark contrast, highlighting the birds' silhouettes and making them stand out prominently.

I added Mistral's API prices to my tools.simonwillison.net/llm-prices pricing calculator by pasting screenshots of Mistral's pricing tables into Claude.

# 17th March 2025, 6:45 pm / vision-llms, mistral, llm, generative-ai, ai, llms, ai-assisted-programming, llm-release

Mistral OCR (via) New closed-source specialist OCR model by Mistral - you can feed it images or a PDF and it produces Markdown with optional embedded images.

It's available via their API, or it's "available to self-host on a selective basis" for people with stringent privacy requirements who are willing to talk to their sales team.

I decided to try out their API, so I copied and pasted example code from their notebook into my custom Claude project and told it:

Turn this into a CLI app, depends on mistralai - it should take a file path and an optional API key defauling to env vironment called MISTRAL_API_KEY

After some further iteration / vibe coding I got to something that worked, which I then tidied up and shared as mistral_ocr.py.

You can try it out like this:

export MISTRAL_API_KEY='...'
uv run http://tools.simonwillison.net/python/mistral_ocr.py \
  mixtral.pdf --html --inline-images > mixtral.html

I fed in the Mixtral paper as a PDF. The API returns Markdown, but my --html option renders that Markdown as HTML and the --inline-images option takes any images and inlines them as base64 URIs (inspired by monolith). The result is mixtral.html, a 972KB HTML file with images and text bundled together.

This did a pretty great job!

My script renders Markdown tables but I haven't figured out how to render inline Markdown MathML yet. I ran the command a second time and requested Markdown output (the default) like this:

uv run http://tools.simonwillison.net/python/mistral_ocr.py \
  mixtral.pdf > mixtral.md

Here's that Markdown rendered as a Gist - there are a few MathML glitches so clearly the Mistral OCR MathML dialect and the GitHub Formatted Markdown dialect don't quite line up.

My tool can also output raw JSON as an alternative to Markdown or HTML - full details in the documentation.

The Mistral API is priced at roughly 1000 pages per dollar, with a 50% discount for batch usage.

The big question with LLM-based OCR is always how well it copes with accidental instructions in the text (can you safely OCR a document full of prompting examples?) and how well it handles text it can't write.

Mistral's Sophia Yang says it "should be robust" against following instructions in the text, and invited people to try and find counter-examples.

Alexander Doria noted that Mistral OCR can hallucinate text when faced with handwriting that it cannot understand.

# 7th March 2025, 1:39 am / vision-llms, mistral, pdf, generative-ai, ocr, ai, llms, projects, claude, uv

llm-ollama 0.9.0. This release of the llm-ollama plugin adds support for schemas, thanks to a PR by Adam Compton.

Ollama provides very robust support for this pattern thanks to their structured outputs feature, which works across all of the models that they support by intercepting the logic that outputs the next token and restricting it to only tokens that would be valid in the context of the provided schema.

With Ollama and llm-ollama installed you can run even run structured schemas against vision prompts for local models. Here's one against Ollama's llama3.2-vision:

llm -m llama3.2-vision:latest \
  'describe images' \
  --schema 'species,description,count int' \
  -a https://static.simonwillison.net/static/2025/two-pelicans.jpg

I got back this:

{
    "species": "Pelicans",
    "description": "The image features a striking brown pelican with its distinctive orange beak, characterized by its large size and impressive wingspan.",
    "count": 1
}

(Actually a bit disappointing, as there are two pelicans and their beaks are brown.)

# 4th March 2025, 7:17 am / llm, ollama, plugins, generative-ai, ai, llms, llama, vision-llms

olmOCR (via) New from Ai2 - olmOCR is "an open-source tool designed for high-throughput conversion of PDFs and other documents into plain text while preserving natural reading order".

At its core is allenai/olmOCR-7B-0225-preview, a Qwen2-VL-7B-Instruct variant trained on ~250,000 pages of diverse PDF content (both scanned and text-based) that were labelled using GPT-4o and made available as the olmOCR-mix-0225 dataset.

The olmocr Python library can run the model on any "recent NVIDIA GPU". I haven't managed to run it on my own Mac yet - there are GGUFs out there but it's not clear to me how to run vision prompts through them - but Ai2 offer an online demo which can handle up to ten pages for free.

Given the right hardware this looks like a very inexpensive way to run large scale document conversion projects:

We carefully optimized our inference pipeline for large-scale batch processing using SGLang, enabling olmOCR to convert one million PDF pages for just $190 - about 1/32nd the cost of using GPT-4o APIs.

The most interesting idea from the technical report (PDF) is something they call "document anchoring":

Document anchoring extracts coordinates of salient elements in each page (e.g., text blocks and images) and injects them alongside raw text extracted from the PDF binary file. [...]

Document anchoring processes PDF document pages via the PyPDF library to extract a representation of the page’s structure from the underlying PDF. All of the text blocks and images in the page are extracted, including position information. Starting with the most relevant text blocks and images, these are sampled and added to the prompt of the VLM, up to a defined maximum character limit. This extra information is then available to the model when processing the document.

The one limitation of olmOCR at the moment is that it doesn't appear to do anything with diagrams, figures or illustrations. Vision models are actually very good at interpreting these now, so my ideal OCR solution would include detailed automated descriptions of this kind of content in the resulting text.

Update: Jonathan Soma figured out how to run it on a Mac using LM Studio and the olmocr Python package.

# 26th February 2025, 2:04 am / vision-llms, ai, qwen, llms, fine-tuning, pdf, generative-ai, ocr, ai2

DeepSeek Janus-Pro. Another impressive model release from DeepSeek. Janus is their series of "unified multimodal understanding and generation models" - these are models that can both accept images as input and generate images for output.

Janus-Pro is the new 7B model, which DeepSeek describe as "an advanced version of Janus, improving both multimodal understanding and visual generation significantly". It's released under the not fully open source DeepSeek license.

Janus-Pro is accompanied by this paper, which includes this note about the training:

Our Janus is trained and evaluated using HAI-LLM, which is a lightweight and efficient distributed training framework built on top of PyTorch. The whole training process took about 7/14 days on a cluster of 16/32 nodes for 1.5B/7B model, each equipped with 8 Nvidia A100 (40GB) GPUs.

It includes a lot of high benchmark scores, but closes with some notes on the model's current limitations:

In terms of multimodal understanding, the input resolution is limited to 384 × 384, which affects its performance in fine-grained tasks such as OCR. For text-to-image generation, the low resolution, combined with reconstruction losses introduced by the vision tokenizer, results in images that, while rich in semantic content, still lack fine details. For example, small facial regions occupying limited image space may appear under-detailed. Increasing the image resolution could mitigate these issues.

The easiest way to try this one out is using the Hugging Face Spaces demo. I tried the following prompt for the image generation capability:

A photo of a raccoon holding a handwritten sign that says "I love trash"

And got back this image:

It's now also been ported to Transformers.js, which means you can run the 1B model directly in a WebGPU browser such as Chrome here at webml-community/janus-pro-webgpu (loads about 2.24 GB of model files).

# 27th January 2025, 9:49 pm / vision-llms, generative-ai, deepseek, ai, llms, transformers-js, multi-modal-output, llm-release

Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL! Hot on the heels of yesterday's Qwen2.5-1M, here's Qwen2.5 VL (with an excitable announcement title) - the latest in Qwen's series of vision LLMs.

They're releasing multiple versions: base models and instruction tuned models at 3B, 7B and 72B. There's a lot of additional information in their README.

The 72B model can be selected on Qwen's Qwen Chat interface. I gave it this image of our dog Cleo joy rolling on the beach:

And prompted:

Describe this image in detail, be flowery

(Since the Qwen team tends to use flowery language in their announcements.)

Here's what I got back:

In this enchanting tableau, a canine companion finds solace in the embrace of nature's softest bed. The sandy expanse stretches out like an endless canvas, its surface etched with the delicate imprints of countless wanderers who have come and gone, leaving behind a tapestry of footprints that whisper tales of joyous romps and serene strolls.

The dog, a vision of contentment, lies sprawled upon the sand, its dark fur gleaming under the gentle caress of the sun. Its body is relaxed, limbs splayed in abandon, as if it has surrendered to the soothing rhythm of the beach. A vibrant red collar adorns its neck, a splash of color against the muted tones of the surroundings, hinting at the care and love bestowed upon this cherished pet.

The sand itself is a work of art, its grains shimmering with a silvery sheen that catches the light. Here and there, small tufts of seaweed peek through, remnants of the ocean's bounty, adding a touch of wild beauty to the scene. The shadows cast by unseen objects dance across the sand, creating a play of light and dark that lends an air of mystery to the tranquil setting.

This moment, frozen in time, speaks of peace and harmony, where man's best friend finds respite in the simple pleasures of life. It is a scene that invites one to pause, breathe deeply, and revel in the serenity of nature's embrace.

I'm looking forward to trying out their smaller models - the 3B model in particular should be small enough to run comfortably even on an iPhone, so hopefully someone will wire up an example of that soon (maybe using MLX).

VB points out that the vision benchmarks for Qwen 2.5 VL 7B show it out-performing GPT-4o mini!

Qwen2.5 VL cookbooks

Qwen also just published a set of cookbook recipes:

• universal_recognition.ipynb demonstrates basic visual Q&A, including prompts like Who are these in this picture? Please give their names in Chinese and English against photos of celebrities, an ability other models have deliberately suppressed.
• spatial_understanding.ipynb demonstrates bounding box support, with prompts like Locate the top right brown cake, output its bbox coordinates using JSON format.
• video_understanding.ipynb breaks a video into individual frames and asks questions like Could you go into detail about the content of this long video?
• ocr.ipynb shows Qwen2.5-VL-7B-Instruct performing OCR in multiple different languages.
• document_parsing.ipynb uses Qwen to convert images of documents to HTML and other formats, and notes that "we introduce a unique Qwenvl HTML format that includes positional information for each component, enabling precise document reconstruction and manipulation."
• mobile_agent.ipynb runs Qwen with tool use against tools for controlling a mobile phone, similar to ChatGPT Operator or Claude Computer Use.
• computer_use.ipynb showcases "GUI grounding" - feeding in screenshots of a user's desktop and running tools for things like left clicking on a specific coordinate.

Running it with mlx-vlm

Update 30th January 2025: I got it working on my Mac using uv and mlx-vlm, with some hints from this issue. Here's the recipe that worked (downloading a 9GB model from mlx-community/Qwen2.5-VL-7B-Instruct-8bit):

uv run --with 'numpy<2' --with 'git+https://github.com/huggingface/transformers' \
  --with mlx-vlm \
  python -m mlx_vlm.generate \
    --model mlx-community/Qwen2.5-VL-7B-Instruct-8bit \
    --max-tokens 100 \
    --temp 0.0 \
    --prompt "Describe this image." \
    --image path-to-image.png

I ran that against this image:

And got back this result:

The image appears to illustrate a flowchart or diagram related to a cybersecurity scenario. Here's a breakdown of the elements:

1. User: Represented by a simple icon of a person.
2. AI Agent: Represented by a blue diamond shape.
3. Attacker: Represented by a red icon of a person.
4. Cloud and Email Icons: Represented by a cloud and an envelope, indicating data or information being transferred.
5. Text: The text

# 27th January 2025, 8:05 pm / vision-llms, generative-ai, ai, qwen, llms, prompt-engineering, ocr, uv, mlx

The leading AI models are now very good historians (via) UC Santa Cruz's Benjamin Breen (previously) explores how the current crop of top tier LLMs - GPT-4o, o1, and Claude Sonnet 3.5 - are proving themselves competent at a variety of different tasks relevant to academic historians.

The vision models are now capable of transcribing and translating scans of historical documents - in this case 16th century Italian cursive handwriting and medical recipes from 1770s Mexico.

Even more interestingly, the o1 reasoning model was able to produce genuinely useful suggestions for historical interpretations against prompts like this one:

Here are some quotes from William James’ complete works, referencing Francis galton and Karl Pearson. What are some ways we can generate new historical knowledge or interpretations on the basis of this? I want a creative, exploratory, freewheeling analysis which explores the topic from a range of different angles and which performs metacognitive reflection on research paths forward based on this, especially from a history of science and history of technology perspectives. end your response with some further self-reflection and self-critique, including fact checking. then provide a summary and ideas for paths forward. What further reading should I do on this topic? And what else jumps out at you as interesting from the perspective of a professional historian?

How good? He followed-up by asking for "the most creative, boundary-pushing, or innovative historical arguments or analyses you can formulate based on the sources I provided" and described the resulting output like this:

The supposedly “boundary-pushing” ideas it generated were all pretty much what a class of grad students would come up with — high level and well-informed, but predictable.

As Benjamin points out, this is somewhat expected: LLMs "are exquisitely well-tuned machines for finding the median viewpoint on a given issue" - something that's already being illustrated by the sameness of work from his undergraduates who are clearly getting assistance from ChatGPT.

I'd be fascinated to hear more from academics outside of the computer science field who are exploring these new tools in a similar level of depth.

Update: Something that's worth emphasizing about this article: all of the use-cases Benjamin describes here involve feeding original source documents to the LLM as part of their input context. I've seen some criticism of this article that assumes he's asking LLMs to answer questions baked into their weights (as this NeurIPS poster demonstrates, even the best models don't have perfect recall of a wide range of historical facts). That's not what he's doing here.

# 26th January 2025, 10:36 pm / history, generative-ai, benjamin-breen, llms, vision-llms, o1, inference-scaling, ai

OpenAI: Voice mode FAQ. Given how impressed I was by the Gemini 2.0 Flash audio and video streaming demo on Wednesday it's only fair that I highlight that OpenAI shipped their equivalent of that feature to ChatGPT in production on Thursday, for day 6 of their "12 days of OpenAI" series.

I got access in the ChatGPT iPhone app this morning. It's equally impressive: in an advanced voice mode conversation you can now tap the camera icon to start sharing a live video stream with ChatGPT. I introduced it to my chickens and told it their names and it was then able to identify each of them later in that same conversation. Apparently the ChatGPT desktop app can do screen sharing too, though that feature hasn't rolled out to me just yet.

(For the rest of December you can also have it take on a Santa voice and personality - I had Santa read me out Haikus in Welsh about what he could see through my camera earlier.)

Given how cool this is, it's frustrating that there's no obvious page (other than this FAQ) to link to for the announcement of the feature! Surely this deserves at least an article in the OpenAI News blog?

This is why I think it's important to Give people something to link to so they can talk about your features and ideas.

# 13th December 2024, 8 pm / vision-llms, openai, text-to-speech, chatgpt, ai, llms, generative-ai, multi-modal-output

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

Say hello to gemini-exp-1121. Google Gemini's Logan Kilpatrick on Twitter:

Say hello to gemini-exp-1121! Our latest experimental gemini model, with:

• significant gains on coding performance
• stronger reasoning capabilities
• improved visual understanding

Available on Google AI Studio and the Gemini API right now

The 1121 in the name is a release date of the 21st November. This comes fast on the heels of last week's gemini-exp-1114.

Both of these new experimental Gemini models have seen moments at the top of the Chatbot Arena. gemini-exp-1114 took the top spot a few days ago, and then lost it to a new OpenAI model called "ChatGPT-4o-latest (2024-11-20)"... only for the new gemini-exp-1121 to hold the top spot right now.

(These model names are all so, so bad.)

I released llm-gemini 0.4.2 with support for the new model - this should have been 0.5 but I already have a 0.5a0 alpha that depends on an unreleased feature in LLM core.

I tried my pelican benchmark:

llm -m gemini-exp-1121 'Generate an SVG of a pelican riding a bicycle'

Since Gemini is a multi-modal vision model, I had it describe the image it had created back to me (by feeding it a PNG render):

llm -m gemini-exp-1121 describe -a pelican.png

And got this description, which is pretty great:

The image shows a simple, stylized drawing of an insect, possibly a bee or an ant, on a vehicle. The insect is composed of a large yellow circle for the body and a smaller yellow circle for the head. It has a black dot for an eye, a small orange oval for a beak or mouth, and thin black lines for antennae and legs. The insect is positioned on top of a simple black and white vehicle with two black wheels. The drawing is abstract and geometric, using basic shapes and a limited color palette of black, white, yellow, and orange.

Update: Logan confirmed on Twitter that these models currently only have a 32,000 token input, significantly less than the rest of the Gemini family.

# 22nd November 2024, 6:14 am / vision-llms, gemini, llm, google, generative-ai, ai, llms, logan-kilpatrick, pelican-riding-a-bicycle, llm-release

Pixtral Large (via) New today from Mistral:

Today we announce Pixtral Large, a 124B open-weights multimodal model built on top of Mistral Large 2. Pixtral Large is the second model in our multimodal family and demonstrates frontier-level image understanding.

The weights are out on Hugging Face (over 200GB to download, and you'll need a hefty GPU rig to run them). The license is free for academic research but you'll need to pay for commercial usage.

The new Pixtral Large model is available through their API, as models called pixtral-large-2411 and pixtral-large-latest.

Here's how to run it using LLM and the llm-mistral plugin:

llm install -U llm-mistral
llm keys set mistral
# paste in API key
llm mistral refresh
llm -m mistral/pixtral-large-latest describe -a https://static.simonwillison.net/static/2024/pelicans.jpg

The image shows a large group of birds, specifically pelicans, congregated together on a rocky area near a body of water. These pelicans are densely packed together, some looking directly at the camera while others are engaging in various activities such as preening or resting. Pelicans are known for their large bills with a distinctive pouch, which they use for catching fish. The rocky terrain and the proximity to water suggest this could be a coastal area or an island where pelicans commonly gather in large numbers. The scene reflects a common natural behavior of these birds, often seen in their nesting or feeding grounds.

Update: I released llm-mistral 0.8 which adds async model support for the full Mistral line, plus a new llm -m mistral-large shortcut alias for the Mistral Large model.

# 18th November 2024, 4:41 pm / vision-llms, mistral, llm, generative-ai, ai, llms, llm-release

Ollama: Llama 3.2 Vision. Ollama released version 0.4 last week with support for Meta's first Llama vision model, Llama 3.2.

If you have Ollama installed you can fetch the 11B model (7.9 GB) like this:

ollama pull llama3.2-vision

Or the larger 90B model (55GB download, likely needs ~88GB of RAM) like this:

ollama pull llama3.2-vision:90b

I was delighted to learn that Sukhbinder Singh had already contributed support for LLM attachments to Sergey Alexandrov's llm-ollama plugin, which means the following works once you've pulled the models:

llm install --upgrade llm-ollama
llm -m llama3.2-vision:latest 'describe' \
  -a https://static.simonwillison.net/static/2024/pelican.jpg

This image features a brown pelican standing on rocks, facing the camera and positioned to the left of center. The bird's long beak is a light brown color with a darker tip, while its white neck is adorned with gray feathers that continue down to its body. Its legs are also gray.

In the background, out-of-focus boats and water are visible, providing context for the pelican's environment.

That's not a bad description of this image, especially for a 7.9GB model that runs happily on my MacBook Pro.

# 13th November 2024, 1:55 am / vision-llms, llm, llama, ai, edge-llms, llms, meta, ollama, generative-ai, llm-release

Claude API: PDF support (beta) (via) Claude 3.5 Sonnet now accepts PDFs as attachments:

The new Claude 3.5 Sonnet (claude-3-5-sonnet-20241022) model now supports PDF input and understands both text and visual content within documents.

I just released llm-claude-3 0.7 with support for the new attachment type (attachments are a very new feature), so now you can do this:

llm install llm-claude-3 --upgrade
llm -m claude-3.5-sonnet 'extract text' -a mydoc.pdf

Visual PDF analysis can also be turned on for the Claude.ai application:

Also new today: Claude now offers a free (albeit rate-limited) token counting API. This addresses a complaint I've had for a while: previously it wasn't possible to accurately estimate the cost of a prompt before sending it to be executed.

# 1st November 2024, 6:55 pm / vision-llms, claude-3-5-sonnet, llm, anthropic, claude, ai, llms, pdf, generative-ai, projects

Generating Descriptive Weather Reports with LLMs. Drew Breunig produces the first example I've seen in the wild of the new LLM attachments Python API. Drew's Downtown San Francisco Weather Vibes project combines output from a JSON weather API with the latest image from a webcam pointed at downtown San Francisco to produce a weather report "with a style somewhere between Jack Kerouac and J. Peterman".

Here's the Python code that constructs and executes the prompt. The code runs in GitHub Actions.

# 29th October 2024, 11:12 pm / vision-llms, drew-breunig, llm, generative-ai, ai, llms, github-actions, prompt-engineering

LLM Pictionary. Inspired by my SVG pelicans on a bicycle, Paul Calcraft built this brilliant system where different vision LLMs can play Pictionary with each other, taking it in turns to progressively draw SVGs while the other models see if they can guess what the image represents.

# 26th October 2024, 8:19 pm / vision-llms, svg, generative-ai, ai, llms

Running prompts against images and PDFs with Google Gemini. New TIL. I've been experimenting with the Google Gemini APIs for running prompts against images and PDFs (in preparation for finally adding multi-modal support to LLM) - here are my notes on how to send images or PDF files to their API using curl and the base64 -i macOS command.

I figured out the curl incantation first and then got Claude to build me a Bash script that I can execute like this:

prompt-gemini 'extract text' example-handwriting.jpg

Playing with this is really fun. The Gemini models charge less than 1/10th of a cent per image, so it's really inexpensive to try them out.

# 23rd October 2024, 6:25 pm / vision-llms, gemini, llm, bash, ai, llms, ai-assisted-programming, google, generative-ai, ocr, projects, llm-pricing

Gemini 1.5 Flash-8B is now production ready (via) Gemini 1.5 Flash-8B is "a smaller and faster variant of 1.5 Flash" - and is now released to production, at half the price of the 1.5 Flash model.

It's really, really cheap:

• $0.0375 per 1 million input tokens on prompts <128K
• $0.15 per 1 million output tokens on prompts <128K
• $0.01 per 1 million input tokens on cached prompts <128K

Prices are doubled for prompts longer than 128K.

I believe images are still charged at a flat rate of 258 tokens, which I think means a single non-cached image with Flash should cost 0.00097 cents - a number so tiny I'm doubting if I got the calculation right.

OpenAI's cheapest model remains GPT-4o mini, at $0.15/1M input - though that drops to half of that for reused prompt prefixes thanks to their new prompt caching feature (or by half if you use batches, though those can’t be combined with OpenAI prompt caching. Gemini also offer half-off for batched requests).

Anthropic's cheapest model is still Claude 3 Haiku at $0.25/M, though that drops to $0.03/M for cached tokens (if you configure them correctly).

I've released llm-gemini 0.2 with support for the new model:

llm install -U llm-gemini
llm keys set gemini
# Paste API key here
llm -m gemini-1.5-flash-8b-latest "say hi"

# 3rd October 2024, 8:16 pm / vision-llms, gemini, anthropic, openai, ai, llms, google, generative-ai, llm, llm-pricing, prompt-caching, llm-release

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. [...]

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

Llama 3.2. In further evidence that AI labs are terrible at naming things, Llama 3.2 is a huge upgrade to the Llama 3 series - they've released their first multi-modal vision models!

Today, we’re releasing Llama 3.2, which includes small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B) that fit onto edge and mobile devices, including pre-trained and instruction-tuned versions.

The 1B and 3B text-only models are exciting too, with a 128,000 token context length and optimized for edge devices (Qualcomm and MediaTek hardware get called out specifically).

Meta partnered directly with Ollama to help with distribution, here's the Ollama blog post. They only support the two smaller text-only models at the moment - this command will get the 3B model (2GB):

ollama run llama3.2

And for the 1B model (a 1.3GB download):

ollama run llama3.2:1b

I had to first upgrade my Ollama by clicking on the icon in my macOS task tray and selecting "Restart to update".

The two vision models are coming to Ollama "very soon".

Once you have fetched the Ollama model you can access it from my LLM command-line tool like this:

pipx install llm
llm install llm-ollama
llm chat -m llama3.2:1b

I tried running my djp codebase through that tiny 1B model just now and got a surprisingly good result - by no means comprehensive, but way better than I would ever expect from a model of that size:

files-to-prompt **/*.py -c | llm -m llama3.2:1b --system 'describe this code'

Here's a portion of the output:

The first section defines several test functions using the @djp.hookimpl decorator from the djp library. These hook implementations allow you to intercept and manipulate Django's behavior.

• test_middleware_order: This function checks that the middleware order is correct by comparing the MIDDLEWARE setting with a predefined list.
• test_middleware: This function tests various aspects of middleware:
• It retrieves the response from the URL /from-plugin/ using the Client object, which simulates a request to this view.
• It checks that certain values are present in the response:
  • X-DJP-Middleware-After
  • X-DJP-Middleware
  • X-DJP-Middleware-Before [...]

I found the GGUF file that had been downloaded by Ollama in my ~/.ollama/models/blobs directory. The following command let me run that model directly in LLM using the llm-gguf plugin:

llm install llm-gguf
llm gguf register-model ~/.ollama/models/blobs/sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 -a llama321b
llm chat -m llama321b

Meta themselves claim impressive performance against other existing models:

Our evaluation suggests that the Llama 3.2 vision models are competitive with leading foundation models, Claude 3 Haiku and GPT4o-mini on image recognition and a range of visual understanding tasks. The 3B model outperforms the Gemma 2 2.6B and Phi 3.5-mini models on tasks such as following instructions, summarization, prompt rewriting, and tool-use, while the 1B is competitive with Gemma.

Here's the Llama 3.2 collection on Hugging Face. You need to accept the new Llama 3.2 Community License Agreement there in order to download those models.

You can try the four new models out via the Chatbot Arena - navigate to "Direct Chat" there and select them from the dropdown menu. You can upload images directly to the chat there to try out the vision features.

# 25th September 2024, 8:28 pm / meta, vision-llms, generative-ai, llama, ai, llms, ollama, edge-llms, llm-release

Pixtral 12B. Mistral finally have a multi-modal (image + text) vision LLM!

I linked to their tweet, but there’s not much to see there - in now classic Mistral style they released the new model with an otherwise unlabeled link to a torrent download. A more useful link is mistral-community/pixtral-12b-240910 on Hugging Face, a 25GB “Unofficial Mistral Community” copy of the weights.

Pixtral was announced at Mistral’s AI Summit event in San Francisco today. It has 128,000 token context, is Apache 2.0 licensed and handles 1024x1024 pixel images. They claim it’s particularly good for OCR and information extraction. It’s not available on their La Platforme hosted API yet, but that’s coming soon.

A few more details can be found in the release notes for mistral-common 1.4.0. That’s their open source library of code for working with the models - it doesn’t actually run inference, but it includes the all-important tokenizer, which now includes three new special tokens: [IMG], [IMG_BREAK] and [IMG_END].

# 11th September 2024, 10:18 pm / vision-llms, mistral, generative-ai, ai, llms, llm-release

Qwen2-VL: To See the World More Clearly. Qwen is Alibaba Cloud's organization training LLMs. Their latest model is Qwen2-VL - a vision LLM - and it's getting some really positive buzz. Here's a r/LocalLLaMA thread about the model.

The original Qwen models were licensed under their custom Tongyi Qianwen license, but starting with Qwen2 on June 7th 2024 they switched to Apache 2.0, at least for their smaller models:

While Qwen2-72B as well as its instruction-tuned models still uses the original Qianwen License, all other models, including Qwen2-0.5B, Qwen2-1.5B, Qwen2-7B, and Qwen2-57B-A14B, turn to adopt Apache 2.0

Here's where things get odd: shortly before I first published this post the Qwen GitHub organization, and their GitHub pages hosted blog, both disappeared and returned 404s pages. I asked on Twitter but nobody seems to know what's happened to them.

Update: this was accidental and was resolved on 5th September.

The Qwen Hugging Face page is still up - it's just the GitHub organization that has mysteriously vanished.

Inspired by Dylan Freedman I tried the model using GanymedeNil/Qwen2-VL-7B on Hugging Face Spaces, and found that it was exceptionally good at extracting text from unruly handwriting:

The model apparently runs great on NVIDIA GPUs, and very slowly using the MPS PyTorch backend on Apple Silicon. Qwen previously released MLX builds of their non-vision Qwen2 models, so hopefully there will be an Apple Silicon optimized MLX model for Qwen2-VL soon as well.

# 4th September 2024, 11:16 pm / vision-llms, llms, ai, generative-ai, qwen, llm-release