Simon Willison’s Weblog

Subscribe
Atom feed for llm

114 items tagged “llm”

LLM is my command-line tool for running prompts against Large Language Models.

2024

llm-openrouter 0.3. New release of my llm-openrouter plugin, which allows LLM to access models hosted by OpenRouter.

Quoting the release notes:

  • Enable image attachments for models that support images. Thanks, Adam Montgomery. #12
  • Provide async model access. #15
  • Fix documentation to list correct LLM_OPENROUTER_KEY environment variable. #10

# 8th December 2024, 11:56 pm / llm, releases, plugins, generative-ai, ai, llms

Prompts.js

Visit Prompts.js

I’ve been putting the new o1 model from OpenAI through its paces, in particular for code. I’m very impressed—it feels like it’s giving me a similar code quality to Claude 3.5 Sonnet, at least for Python and JavaScript and Bash... but it’s returning output noticeably faster.

[... 1,118 words]

New Gemini model: gemini-exp-1206. Google's Jeff Dean:

Today’s the one year anniversary of our first Gemini model releases! And it’s never looked better.

Check out our newest release, Gemini-exp-1206, in Google AI Studio and the Gemini API!

I upgraded my llm-gemini plugin to support the new model and released it as version 0.6 - you can install or upgrade it like this:

llm install -U llm-gemini

Running my SVG pelican on a bicycle test prompt:

llm -m gemini-exp-1206 "Generate an SVG of a pelican riding a bicycle"

Provided this result, which is the best I've seen from any model:

Blue sky, green grass, bicycle looks good, bird riding it is almost recognizable as a pelican

Here's the full output - I enjoyed these two pieces of commentary from the model:

<polygon>: Shapes the distinctive pelican beak, with an added line for the lower mandible.
[...]
transform="translate(50, 30)": This attribute on the pelican's <g> tag moves the entire pelican group 50 units to the right and 30 units down, positioning it correctly on the bicycle.

The new model is also currently in top place on the Chatbot Arena.

Update: a delightful bonus, here's what I got from the follow-up prompt:

llm -c "now animate it"

The pelican is now animated - it is pedaling and its wing moves

Transcript here.

# 6th December 2024, 6:05 pm / gemini, llm, ai, llms, svg, releases, google, generative-ai, pelican-riding-a-bicycle

datasette-enrichments-llm. Today's new alpha release is datasette-enrichments-llm, a plugin for Datasette 1.0a+ that provides an enrichment that lets you run prompts against data from one or more column and store the result in another column.

So far it's a light re-implementation of the existing datasette-enrichments-gpt plugin, now using the new llm.get_async_models() method to allow users to select any async-enabled model that has been registered by a plugin - so currently any of the models from OpenAI, Anthropic, Gemini or Mistral via their respective plugins.

Still plenty to do on this one. Next step is to integrate it with datasette-llm-usage and use it to drive a design-complete stable version of that.

# 5th December 2024, 11:46 pm / llm, plugins, ai, llms, enrichments, releases, datasette, generative-ai, projects

First impressions of the new Amazon Nova LLMs (via a new llm-bedrock plugin)

Visit First impressions of the new Amazon Nova LLMs (via a new llm-bedrock plugin)

Amazon released three new Large Language Models yesterday at their AWS re:Invent conference. The new model family is called Amazon Nova and comes in three sizes: Micro, Lite and Pro.

[... 2,385 words]

datasette-queries. I released the first alpha of a new plugin to replace the crusty old datasette-saved-queries. This one adds a new UI element to the top of the query results page with an expandable form for saving the query as a new canned query:

Animated demo. I start on the table page, run a search, click View and edit SQL, then on the SQL query page open a Save query dialog, click a Suggest title and description button, wait for that to suggest something and click save.

It's my first plugin to depend on LLM and datasette-llm-usage - it uses GPT-4o mini to power an optional "Suggest title and description" button, labeled with the becoming-standard ✨ sparkles emoji to indicate an LLM-powered feature.

I intend to expand this to work across multiple models as I continue to iterate on llm-datasette-usage to better support those kinds of patterns.

For the moment though each suggested title and description call costs about 250 input tokens and 50 output tokens, which against GPT-4o mini adds up to 0.0067 cents.

# 3rd December 2024, 11:59 pm / projects, releases, datasette, plugins, llm, generative-ai, openai, ai, llms

datasette-llm-usage. I released the first alpha of a Datasette plugin to help track LLM usage by other plugins, with the goal of supporting token allowances - both for things like free public apps that stop working after a daily allowance, plus free previews of AI features for paid-account-based projects such as Datasette Cloud.

It's using the usage features I added in LLM 0.19.

The alpha doesn't do much yet - it will start getting interesting once I upgrade other plugins to depend on it.

Design notes so far in issue #1.

# 2nd December 2024, 9:33 pm / llm, datasette-cloud, plugins, ai, llms, datasette, generative-ai, projects, releases

PydanticAI (via) New project from Pydantic, which they describe as an "Agent Framework / shim to use Pydantic with LLMs".

I asked which agent definition they are using and it's the "system prompt with bundled tools" one. To their credit, they explain that in their documentation:

The Agent has full API documentation, but conceptually you can think of an agent as a container for:

  • A system prompt — a set of instructions for the LLM written by the developer
  • One or more retrieval tool — functions that the LLM may call to get information while generating a response
  • An optional structured result type — the structured datatype the LLM must return at the end of a run

Given how many other existing tools already lean on Pydantic to help define JSON schemas for talking to LLMs this is an interesting complementary direction for Pydantic to take.

There's some overlap here with my own LLM project, which I still hope to add a function calling / tools abstraction to in the future.

# 2nd December 2024, 9:08 pm / llm, python, generative-ai, agents, llms

LLM 0.19. I just released version 0.19 of LLM, my Python library and CLI utility for working with Large Language Models.

I released 0.18 a couple of weeks ago adding support for calling models from Python asyncio code. 0.19 improves on that, and also adds a new mechanism for models to report their token usage.

LLM can log those usage numbers to a SQLite database, or make then available to custom Python code.

My eventual goal with these features is to implement token accounting as a Datasette plugin so I can offer AI features in my SaaS platform without worrying about customers spending unlimited LLM tokens.

Those 0.19 release notes in full:

  • Tokens used by a response are now logged to new input_tokens and output_tokens integer columns and a token_details JSON string column, for the default OpenAI models and models from other plugins that implement this feature. #610
  • llm prompt now takes a -u/--usage flag to display token usage at the end of the response.
  • llm logs -u/--usage shows token usage information for logged responses.
  • llm prompt ... --async responses are now logged to the database. #641
  • llm.get_models() and llm.get_async_models() functions, documented here. #640
  • response.usage() and async response await response.usage() methods, returning a Usage(input=2, output=1, details=None) dataclass. #644
  • response.on_done(callback) and await response.on_done(callback) methods for specifying a callback to be executed when a response has completed, documented here. #653
  • Fix for bug running llm chat on Windows 11. Thanks, Sukhbinder Singh. #495

I also released three new plugin versions that add support for the new usage tracking feature: llm-gemini 0.5, llm-claude-3 0.10 and llm-mistral 0.9.

# 1st December 2024, 11:59 pm / llm, releasenotes, generative-ai, projects, ai, llms, releases

QwQ: Reflect Deeply on the Boundaries of the Unknown. Brand new openly licensed (Apache 2) model from Alibaba Cloud's Qwen team, this time clearly inspired by OpenAI's work on reasoning in o1.

I love the flowery language they use to introduce the new model:

Through deep exploration and countless trials, we discovered something profound: when given time to ponder, to question, and to reflect, the model’s understanding of mathematics and programming blossoms like a flower opening to the sun. Just as a student grows wiser by carefully examining their work and learning from mistakes, our model achieves deeper insight through patient, thoughtful analysis.

It's already available through Ollama as a 20GB download. I initially ran it like this:

ollama run qwq

This downloaded the model and started an interactive chat session. I tried the classic "how many rs in strawberry?" and got this lengthy but correct answer, which concluded:

Wait, but maybe I miscounted. Let's list them: 1. s 2. t 3. r 4. a 5. w 6. b 7. e 8. r 9. r 10. y Yes, definitely three "r"s. So, the word "strawberry" contains three "r"s.

Then I switched to using LLM and the llm-ollama plugin. I tried prompting it for Python that imports CSV into SQLite:

Write a Python function import_csv(conn, url, table_name) which acceopts a connection to a SQLite databse and a URL to a CSV file and the name of a table - it then creates that table with the right columns and imports the CSV data from that URL

It thought through the different steps in detail and produced some decent looking code.

Finally, I tried this:

llm -m qwq 'Generate an SVG of a pelican riding a bicycle'

For some reason it answered in Simplified Chinese. It opened with this:

生成一个SVG图像,内容是一只鹈鹕骑着一辆自行车。这听起来挺有趣的!我需要先了解一下什么是SVG,以及如何创建这样的图像。

Which translates (using Google Translate) to:

Generate an SVG image of a pelican riding a bicycle. This sounds interesting! I need to first understand what SVG is and how to create an image like this.

It then produced a lengthy essay discussing the many aspects that go into constructing a pelican on a bicycle - full transcript here. After a full 227 seconds of constant output it produced this as the final result.

You can tell which bit is the bicycle and which bit is the pelican. It's quite elegant.

I think that's pretty good!

# 27th November 2024, 11:59 pm / llm, ollama, generative-ai, ai, qwen, llms, edge-llms, pelican-riding-a-bicycle

Ask questions of SQLite databases and CSV/JSON files in your terminal

Visit Ask questions of SQLite databases and CSV/JSON files in your terminal

I built a new plugin for my sqlite-utils CLI tool that lets you ask human-language questions directly of SQLite databases and CSV/JSON files on your computer.

[... 723 words]

Weeknotes: asynchronous LLMs, synchronous embeddings, and I kind of started a podcast

These past few weeks I’ve been bringing Datasette and LLM together and distracting myself with a new sort-of-podcast crossed with a live streaming experiment.

[... 896 words]

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'
Not great at all, description follows

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-gguf 0.2, now with embeddings. This new release of my llm-gguf plugin - which provides support for locally hosted GGUF LLMs - adds a new feature: it now supports embedding models distributed as GGUFs as well.

This means you can use models like the bafflingly small (30.8MB in its smallest quantization) mxbai-embed-xsmall-v1 with LLM like this:

llm install llm-gguf
llm gguf download-embed-model \
  'https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1/resolve/main/gguf/mxbai-embed-xsmall-v1-q8_0.gguf'

Then to embed a string:

llm embed -m gguf/mxbai-embed-xsmall-v1-q8_0 -c 'hello'

The LLM docs have extensive coverage of things you can then do with this model, like embedding every row in a CSV file / file in a directory / record in a SQLite database table and running similarity and semantic search against them.

Under the hood this takes advantage of the create_embedding() method provided by the llama-cpp-python wrapper around llama.cpp.

# 21st November 2024, 7:24 am / llm, generative-ai, projects, ai, embeddings

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.

A photo I took of some pelicans

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-gemini 0.4. New release of my llm-gemini plugin, adding support for asynchronous models (see LLM 0.18), plus the new gemini-exp-1114 model (currently at the top of the Chatbot Arena) and a -o json_object 1 option to force JSON output.

I also released llm-claude-3 0.9 which adds asynchronous support for the Claude family of models.

# 18th November 2024, 7:37 am / llm, plugins, ai, llms, async, python, generative-ai, projects, claude, gemini, anthropic, google

LLM 0.18. New release of LLM. The big new feature is asynchronous model support - you can now use supported models in async Python code like this:

import llm

model = llm.get_async_model("gpt-4o")
async for chunk in model.prompt(
    "Five surprising names for a pet pelican"
):
    print(chunk, end="", flush=True)

Also new in this release: support for sending audio attachments to OpenAI's gpt-4o-audio-preview model.

# 17th November 2024, 8:40 pm / async, llm, python, generative-ai, projects, ai, llms

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

Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac

Visit Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac

There’s a whole lot of buzz around the new Qwen2.5-Coder Series of open source (Apache 2.0 licensed) LLM releases from Alibaba’s Qwen research team. On first impression it looks like the buzz is well deserved.

[... 697 words]

Generating documentation from tests using files-to-prompt and LLM. I was experimenting with the wasmtime-py Python library today (for executing WebAssembly programs from inside CPython) and I found the existing API docs didn't quite show me what I wanted to know.

The project has a comprehensive test suite so I tried seeing if I could generate documentation using that:

cd /tmp
git clone https://github.com/bytecodealliance/wasmtime-py
files-to-prompt -e py wasmtime-py/tests -c | \
  llm -m claude-3.5-sonnet -s \
  'write detailed usage documentation including realistic examples'

More notes in my TIL. You can see the full Claude transcript here - I think this worked really well!

# 5th November 2024, 10:37 pm / llm, webassembly, generative-ai, ai, llms, claude, claude-3-5-sonnet, ai-assisted-programming, documentation

Claude 3.5 Haiku

Visit Claude 3.5 Haiku

Anthropic released Claude 3.5 Haiku today, a few days later than expected (they said it would be out by the end of October).

[... 478 words]

Nous Hermes 3. The Nous Hermes family of fine-tuned models have a solid reputation. Their most recent release came out in August, based on Meta's Llama 3.1:

Our training data aggressively encourages the model to follow the system and instruction prompts exactly and in an adaptive manner. Hermes 3 was created by fine-tuning Llama 3.1 8B, 70B and 405B, and training on a dataset of primarily synthetically generated responses. The model boasts comparable and superior performance to Llama 3.1 while unlocking deeper capabilities in reasoning and creativity.

The model weights are on Hugging Face, including GGUF versions of the 70B and 8B models. Here's how to try the 8B model (a 4.58GB download) using the llm-gguf plugin:

llm install llm-gguf
llm gguf download-model 'https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B-GGUF/resolve/main/Hermes-3-Llama-3.1-8B.Q4_K_M.gguf' -a Hermes-3-Llama-3.1-8B
llm -m Hermes-3-Llama-3.1-8B 'hello in spanish'

Nous Research partnered with Lambda Labs to provide inference APIs. It turns out Lambda host quite a few models now, currently providing free inference to users with an API key.

I just released the first alpha of a llm-lambda-labs plugin. You can use that to try the larger 405b model (very hard to run on a consumer device) like this:

llm install llm-lambda-labs
llm keys set lambdalabs
# Paste key here
llm -m lambdalabs/hermes3-405b 'short poem about a pelican with a twist'

Here's the source code for the new plugin, which I based on llm-mistral. The plugin uses httpx-sse to consume the stream of tokens from the API.

# 4th November 2024, 6:20 pm / llm, generative-ai, llama, ai, edge-llms, llms, meta, projects, nous-research

SmolLM2 (via) New from Loubna Ben Allal and her research team at Hugging Face:

SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. [...]

It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon.

The model weights are released under an Apache 2 license. I've been trying these out using my llm-gguf plugin for LLM and my first impressions are really positive.

Here's a recipe to run a 1.7GB Q8 quantized model from lmstudio-community:

llm install llm-gguf
llm gguf download-model https://huggingface.co/lmstudio-community/SmolLM2-1.7B-Instruct-GGUF/resolve/main/SmolLM2-1.7B-Instruct-Q8_0.gguf -a smol17
llm chat -m smol17

Animated terminal demo. My prompt is tell me about pelicans. The model responds: Sure, I'd be happy to tell you about pelicans! Pelicans are a group of aquatic birds in the order Pelecaniformes, which also includes the cormorants, darters, and frigatebirds. They are found on all continents except Antarctica, and are known for their distinctive pouch-like bill. There are several species of pelicans. The most common species is the Brown Pelican, which is found in the Americas. It's the only species that plunges into water from a significant height to catch fish and other prey, a behavior known as "fish-grabbing."  Another common species is the American White Pelican, which can be found in both the Americas and Eurasia. It has a white plumage and a large, bright pink bill, and feeds on fish in lakes, rivers, and coastal wetlands.  Pelicans are generally medium-sized birds, but the Brown Pelican is the largest, with an average height of around 26-30 inches. Their bills can be as long as 11 inches!  Below the terminal you can see Activity Monitor showing 378% CPU usage for the Python process

Or at the other end of the scale, here's how to run the 138MB Q8 quantized 135M model:

llm gguf download-model https://huggingface.co/lmstudio-community/SmolLM2-135M-Instruct-GGUF/resolve/main/SmolLM2-135M-Instruct-Q8_0.gguf' -a smol135m
llm chat -m smol135m

The blog entry to accompany SmolLM2 should be coming soon, but in the meantime here's the entry from July introducing the first version: SmolLM - blazingly fast and remarkably powerful .

# 2nd November 2024, 5:27 am / llm, hugging-face, generative-ai, ai, llms, open-source, edge-llms, smollm

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:

Screenshot of a feature preview interface showing experimental features. At top: Feature Preview with beaker icon. Main text explains these are upcoming enhancements that may affect Claude's behavior. Shows options for Analysis tool, LaTeX Rendering, and Visual PDFs. Right panel demonstrates Visual PDFs feature with Apollo 17 flight plan image and chat messages. Toggle switch shows feature is Off. Description states Give Claude 3.5 Sonnet the ability to view and analyze images, charts, and graphs in PDFs, in addition to text. PDFs that are less than 100 pages are supported.

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

docs.jina.ai—the Jina meta-prompt. From Jina AI on Twitter:

curl docs.jina.ai - This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set.

The page is served using content negotiation. If you hit it with curl you get plain text, but a browser with text/html in the accept: header gets an explanation along with a convenient copy to clipboard button.

Screenshot of an API documentation page for Jina AI with warning message, access instructions, and code sample. Contains text: Note: This content is specifically designed for LLMs and not intended for human reading. For human-readable content, please visit Jina AI. For LLMs/programmatic access, you can fetch this content directly: curl docs.jina.ai/v2 # or wget docs.jina.ai/v2 # or fetch docs.jina.ai/v2 You only see this as a HTML when you access docs.jina.ai via browser. If you access it via code/program, you will get a text/plain response as below. You are an AI engineer designed to help users use Jina AI Search Foundation API's for their specific use case. # Core principles...

# 30th October 2024, 5:07 pm / llm, jina, generative-ai, ai, documentation, llms

W̶e̶e̶k̶n̶o̶t̶e̶s̶ Monthnotes for October

I try to publish weeknotes at least once every two weeks. It’s been four since the last entry, so I guess this one counts as monthnotes instead.

[... 797 words]

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

You can now run prompts against images, audio and video in your terminal using LLM

Visit You can now run prompts against images, audio and video in your terminal using LLM

I released LLM 0.17 last night, the latest version of my combined CLI tool and Python library for interacting with hundreds of different Large Language Models such as GPT-4o, Llama, Claude and Gemini.

[... 1,399 words]

python-imgcat (via) I was investigating options for displaying images in a terminal window (for multi-modal logging output of LLM) and I found this neat Python library for displaying images using iTerm 2.

It includes a CLI tool, which means you can run it without installation using uvx like this:

uvx imgcat filename.png

Screenshot of an iTerm2 terminal window. I have run uvx imgcat output_4.png and an image is shown below that in the terminal of a slide from a FEMA deck about Tropical Storm Ian.

# 28th October 2024, 5:13 am / llm, cli, python, uv

llm-whisper-api. I wanted to run an experiment through the OpenAI Whisper API this morning so I knocked up a very quick plugin for LLM that provides the following interface:

llm install llm-whisper-api
llm whisper-api myfile.mp3 > transcript.txt

It uses the API key that you previously configured using the llm keys set openai command. If you haven't configured one you can pass it as --key XXX instead.

It's a tiny plugin: the source code is here.

# 27th October 2024, 6:19 pm / llm, projects, plugins, openai, whisper, ai