Model: vivnatan/legal-ft-20c85cc6-30d1-49ca-97e6-cce1045a4b4a
Author: vivnatan
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:780, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-l, base_model:finetune:Snowflake/snowflake-arctic-embed-l, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-l
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:780, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget: source_sentence: What is one potential negative use of LLMs related to education?
sentences: A year ago, the only organization that had released a generally useful LLM was OpenAI. We’ve now seen better-than-GPT-3 class models produced by Anthropic, Mistral, Google, Meta, EleutherAI, Stability AI, TII in Abu Dhabi (Falcon), Microsoft Research, xAI, Replit, Baidu and a bunch of other organizations.
The training cost (hardware and electricity) is still significant—initially millions of dollars, but that seems to have dropped to the tens of thousands already. Microsoft’s Phi-2 claims to have used “14 days on 96 A100 GPUs”, which works out at around $35,000 using current Lambda pricing., Then in December, the Chatbot Arena team introduced a whole new leaderboard for this feature, driven by users building the same interactive app twice with two different models and voting on the answer. Hard to come up with a more convincing argument that this feature is now a commodity that can be effectively implemented against all of the leading models.
I’ve been tinkering with a version of this myself for my Datasette project, with the goal of letting users use prompts to build and iterate on custom widgets and data visualizations against their own data. I also figured out a similar pattern for writing one-shot Python programs, enabled by uv., Here’s the sequel to this post: Things we learned about LLMs in 2024.
Large Language Models
In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.
LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.
They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes., source_sentence: What is the significance of the UK’s Railway Mania in the context of railway construction?
sentences: An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes!
The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage.
The year of slop, The top five: ai (342), generativeai (300), llms (287), openai (86), chatgpt (78).
I’ve written a lot about this stuff!
I grabbed a screenshot of my Plausible analytics for the year, fed that to ChatGPT Vision, told it to extract the data into a table, then got it to mix in entry titles (from a SQL query it wrote) and produced this table with it. Here are my top entries this year by amount of traffic:



Article
Visitors
Pageviews




Bing: “I will not harm you unless you harm me first”
1.1M
1.3M


Leaked Google document: “We Have No Moat, And Neither Does OpenAI”
132k
162k


Large language models are having their Stable Diffusion moment
121k
150k


Prompt injection: What’s the worst that can happen?
79.8k
95.9k, Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people?
As the quality of results produced by AI models has increased over the year, these questions have become even more pressing.
The impact on human society in terms of these models is already huge, if difficult to objectively measure.
People have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators.
There are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic.
My blog in 2023
Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):, source_sentence: Can you build an LLM without the right data? Why or why not?
sentences: Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!
What matters most is the training  data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is.
If you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM., 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, One way to think about these models is an extension of the chain-of-thought prompting trick, first explored in the May 2022 paper Large Language Models are Zero-Shot Reasoners.
This is that trick where, if you get a model to talk out loud about a problem it’s solving, you often get a result which the model would not have achieved otherwise.
o1 takes this process and further bakes it into the model itself. The details are somewhat obfuscated: o1 models spend “reasoning tokens” thinking through the problem that are not directly visible to the user (though the ChatGPT UI shows a summary of them), then outputs a final result., source_sentence: What was the estimated training cost for DeepSeek v3?
sentences: Now that those features are rolling out they’re pretty weak. As an LLM power-user I know what these models are capable of, and Apple’s LLM features offer a pale imitation of what a frontier LLM can do. Instead we’re getting notification summaries that misrepresent news headlines and writing assistant tools that I’ve not found useful at all. Genmoji are kind of fun though.
The rise of inference-scaling “reasoning” models
The most interesting development in the final quarter of 2024 was the introduction of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as o1-preview and o1-mini on September 12th., Prompt injection is a natural consequence of this gulibility. I’ve seen precious little progress on tackling that problem in 2024, and we’ve been talking about it since September 2022.
I’m beginning to see the most popular idea of “agents” as dependent on AGI itself. A model that’s robust against gulliblity is a very tall order indeed.
Evals really matter
Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):, 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., source_sentence: How does the author describe the clarity of the term "agents"?
sentences: OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do.
That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro.
Since the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return., Things we learned about LLMs in 2024





















Simon Willison’s Weblog
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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:, “Agents” still haven’t really happened yet
I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that.
If you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.
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-l
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.8416666666666667
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.975
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9916666666666667
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8416666666666667
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.32499999999999996
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19833333333333336
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8416666666666667
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.975
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9916666666666667
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.933195287437209
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9105555555555555
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9105555555555557
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 38
Created: 2025-05-05 19:08:28