Model: llm-wizard/state-of-ai-embeddings
Author: llm-wizard
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:156, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-m, base_model:finetune:Snowflake/snowflake-arctic-embed-m, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-m
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:156, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-m
widget: source_sentence: How many input tokens are required for each photo mentioned in the context?
sentences: DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed models currently available, significantly bigger than the largest of Meta’s Llama series, Llama 3.1 405B.
Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models. This is by far the highest ranking openly licensed model.
The really impressive thing about DeepSeek v3 is the training cost. The model was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama 3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model that benchmarks slightly worse., Each photo would need 260 input tokens and around 100 output tokens.
260 * 68,000 = 17,680,000 input tokens
17,680,000 * $0.0375/million = $0.66
100 * 68,000 = 6,800,000 output tokens
6,800,000 * $0.15/million = $1.02
That’s a total cost of $1.68 to process 68,000 images. That’s so absurdly cheap I had to run the numbers three times to confirm I got it right.
How good are those descriptions? Here’s what I got from this command:
llm -m gemini-1.5-flash-8b-latest describe -a IMG_1825.jpeg, The GPT-4 barrier was comprehensively broken
In my December 2023 review I wrote about how We don’t yet know how to build GPT-4—OpenAI’s best model was almost a year old at that point, yet no other AI lab had produced anything better. What did OpenAI know that the rest of us didn’t?
I’m relieved that this has changed completely in the past twelve months. 18 organizations now have models on the Chatbot Arena Leaderboard that rank higher than the original GPT-4 from March 2023 (GPT-4-0314 on the board)—70 models in total., source_sentence: What capabilities does Google’s Gemini have in relation to audio input?
sentences: Things we learned about LLMs in 2024





















Simon Willison’s Weblog
Subscribe






Things we learned about LLMs in 2024
31st December 2024
A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.
This is a sequel to my review of 2023.
In this article:, Your browser does not support the audio element.

OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also accepts audio input, and the Google Gemini apps can speak in a similar way to ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s meant to roll out in Q1 of 2025.
Google’s NotebookLM, released in September, took audio output to a new level by producing spookily realistic conversations between two “podcast hosts” about anything you fed into their tool. They later added custom instructions, so naturally I turned them into pelicans:


Your browser does not support the audio element., In 2024, almost every significant model vendor released multi-modal models. We saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images, audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from OpenAI in October, then November saw SmolVLM from Hugging Face and December saw image and video models from Amazon Nova.
In October I upgraded my LLM CLI tool to support multi-modal models via attachments. It now has plugins for a whole collection of different vision models., source_sentence: What is the mlx-vlm project and how does it relate to vision LLMs on Apple Silicon?
sentences: ai
            1101


            generative-ai
            945


            llms
            933

Next: Tom Scott, and the formidable power of escalating streaks
Previous: Last weeknotes of 2023


 
 


Colophon
©
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025, Prince Canuma’s excellent, fast moving mlx-vlm project brings vision LLMs to Apple Silicon as well. I used that recently to run Qwen’s QvQ.
While MLX is a game changer, Apple’s own “Apple Intelligence” features have mostly been a disappointment. I wrote about their initial announcement in June, and I was optimistic that Apple had focused hard on the subset of LLM applications that preserve user privacy and minimize the chance of users getting mislead by confusing features., Longer inputs dramatically increase the scope of problems that can be solved with an LLM: you can now throw in an entire book and ask questions about its contents, but more importantly you can feed in a lot of example code to help the model correctly solve a coding problem. LLM use-cases that involve long inputs are far more interesting to me than short prompts that rely purely on the information already baked into the model weights. Many of my tools were built using this pattern., source_sentence: What is the term coined by the author to describe the issue of manipulating responses from AI systems?
sentences: Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.
I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!
This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.
Today there are literally thousands of LLMs that can be run locally, on all manner of different devices., On paper, a 64GB Mac should be a great machine for running models due to the way the CPU and GPU can share the same memory. In practice, many models are released as model weights and libraries that reward NVIDIA’s CUDA over other platforms.
The llama.cpp ecosystem helped a lot here, but the real breakthrough has been Apple’s MLX library, “an array framework for Apple Silicon”. It’s fantastic.
Apple’s mlx-lm Python library supports running a wide range of MLX-compatible models on my Mac, with excellent performance. mlx-community on Hugging Face offers more than 1,000 models that have been converted to the necessary format., Sometimes it omits sections of code and leaves you to fill them in, but if you tell it you can’t type because you don’t have any fingers it produces the full code for you instead.
There are so many more examples like this. Offer it cash tips for better answers. Tell it your career depends on it. Give it positive reinforcement. It’s all so dumb, but it works!
Gullibility is the biggest unsolved problem
I coined the term prompt injection in September last year.
15 months later, I regret to say that we’re still no closer to a robust, dependable solution to this problem.
I’ve written a ton about this already.
Beyond that specific class of security vulnerabilities, I’ve started seeing this as a wider problem of gullibility., source_sentence: What is the name of the model that quickly became the author's favorite daily-driver after its launch in March?
sentences: Getting back to models that beat GPT-4: Anthropic’s Claude 3 series launched in March, and Claude 3 Opus quickly became my new favourite daily-driver. They upped the ante even more in June with the launch of Claude 3.5 Sonnet—a model that is still my favourite six months later (though it got a significant upgrade on October 22, confusingly keeping the same 3.5 version number. Anthropic fans have since taken to calling it Claude 3.6)., Embeddings: What they are and why they matter
61.7k
79.3k


Catching up on the weird world of LLMs
61.6k
85.9k


llamafile is the new best way to run an LLM on your own computer
52k
66k


Prompt injection explained, with video, slides, and a transcript
51k
61.9k


AI-enhanced development makes me more ambitious with my projects
49.6k
60.1k


Understanding GPT tokenizers
49.5k
61.1k


Exploring GPTs: ChatGPT in a trench coat?
46.4k
58.5k


Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
40.5k
49.2k


How to implement Q&A against your documentation with GPT3, embeddings and Datasette
37.3k
44.9k


Lawyer cites fake cases invented by ChatGPT, judge is not amused
37.1k
47.4k, We already knew LLMs were spookily good at writing code. If you prompt them right, it turns out they can build you a full interactive application using HTML, CSS and JavaScript (and tools like React if you wire up some extra supporting build mechanisms)—often in a single prompt.
Anthropic kicked this idea into high gear when they released Claude Artifacts, a groundbreaking new feature that was initially slightly lost in the noise due to being described half way through their announcement of the incredible Claude 3.5 Sonnet.
With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface.
Here’s my Extract URLs app, entirely generated by Claude:
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.9166666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.9166666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.9166666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 1
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9692441461309548
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9583333333333334
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9583333333333334
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 83
Created: 2025-02-18 21:40:52