Model: ngiometti/legal-ft-2
Author: ngiometti
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-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:156, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget: source_sentence: How is the author planning to utilize prompts in their Datasette project?
sentences: January

7th: It’s OK to call it Artificial Intelligence

9th: What I should have said about the term Artificial Intelligence

17th: Talking about Open Source LLMs on Oxide and Friends

26th: LLM 0.13: The annotated release notes



February

21st: The killer app of Gemini Pro 1.5 is video



March

5th: Prompt injection and jailbreaking are not the same thing

8th: The GPT-4 barrier has finally been broken

22nd: Claude and ChatGPT for ad-hoc sidequests

23rd: Building and testing C extensions for SQLite with ChatGPT Code Interpreter

26th: llm cmd undo last git commit—a new plugin for LLM



April

8th: Building files-to-prompt entirely using Claude 3 Opus

10th: Three major LLM releases in 24 hours (plus weeknotes), 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., Another common technique is to use larger models to help create training data for their smaller, cheaper alternatives—a trick used by an increasing number of labs. DeepSeek v3 used “reasoning” data created by DeepSeek-R1. Meta’s Llama 3.3 70B fine-tuning used over 25M synthetically generated examples.
Careful design of the training data that goes into an LLM appears to be the entire game for creating these models. The days of just grabbing a full scrape of the web and indiscriminately dumping it into a training run are long gone.
LLMs somehow got even harder to use, source_sentence: What are the potential pitfalls of using LLMs as power-user tools?
sentences: Another common technique is to use larger models to help create training data for their smaller, cheaper alternatives—a trick used by an increasing number of labs. DeepSeek v3 used “reasoning” data created by DeepSeek-R1. Meta’s Llama 3.3 70B fine-tuning used over 25M synthetically generated examples.
Careful design of the training data that goes into an LLM appears to be the entire game for creating these models. The days of just grabbing a full scrape of the web and indiscriminately dumping it into a training run are long gone.
LLMs somehow got even harder to use, A drum I’ve been banging for a while is that LLMs are power-user tools—they’re chainsaws disguised as kitchen knives. They look deceptively simple to use—how hard can it be to type messages to a chatbot?—but in reality you need a huge depth of both understanding and experience to make the most of them and avoid their many pitfalls.
If anything, this problem got worse in 2024.
We’ve built computer systems you can talk to in human language, that will answer your questions and usually get them right! ... depending on the question, and how you ask it, and whether it’s accurately reflected in the undocumented and secret training set., These abilities are just a few weeks old at this point, and I don’t think their impact has been fully felt yet. If you haven’t tried them out yet you really should.
Both Gemini and OpenAI offer API access to these features as well. OpenAI started with a WebSocket API that was quite challenging to use, but in December they announced a new WebRTC API which is much easier to get started with. Building a web app that a user can talk to via voice is easy now!
Prompt driven app generation is a commodity already
This was possible with GPT-4 in 2023, but the value it provides became evident in 2024., source_sentence: What challenges are associated with using LLMs in the year of slop?
sentences: So far, I think they’re a net positive. I’ve used them on a personal level to improve my productivity (and entertain myself) in all sorts of different ways. I think people who learn how to use them effectively can gain a significant boost to their quality of life.
A lot of people are yet to be sold on their value! Some think their negatives outweigh their positives, some think they are all hot air, and some even think they represent an existential threat to humanity.
They’re actually quite easy to build
The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build., The year of slop
Synthetic training data works great
LLMs somehow got even harder to use
Knowledge is incredibly unevenly distributed
LLMs need better criticism
Everything tagged “llms” on my blog in 2024, Meta’s Llama 3.2 models deserve a special mention. They may not be GPT-4 class, but at 1B and 3B sizes they punch massively above their weight. I run Llama 3.2 3B on my iPhone using the free MLC Chat iOS app and it’s a shockingly capable model for its tiny (<2GB) size. Try firing it up and asking it for “a plot outline of a Netflix Christmas movie where a data journalist falls in love with a local ceramacist”. Here’s what I got, at a respectable 20 tokens per second:, source_sentence: What capabilities does Google’s Gemini have regarding audio input and output?
sentences: There’s a flipside to this too: a lot of better informed people have sworn off LLMs entirely because they can’t see how anyone could benefit from a tool with so many flaws. The key skill in getting the most out of LLMs is learning to work with tech that is both inherently unreliable and incredibly powerful at the same time. This is a decidedly non-obvious skill to acquire!
There is so much space for helpful education content here, but we need to do do a lot better than outsourcing it all to AI grifters with bombastic Twitter threads.
Knowledge is incredibly unevenly distributed
Most people have heard of ChatGPT by now. How many have heard of Claude?, There’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible.
Here’s a fun napkin calculation: how much would it cost to generate short descriptions of every one of the 68,000 photos in my personal photo library using Google’s Gemini 1.5 Flash 8B (released in October), their cheapest model?
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, 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., source_sentence: What improvements were noted in the intonation of ChatGPT Advanced Voice mode during its rollout?
sentences: When ChatGPT Advanced Voice mode finally did roll out (a slow roll from August through September) it was spectacular. I’ve been using it extensively on walks with my dog and it’s amazing how much the improvement in intonation elevates the material. I’ve also had a lot of fun experimenting with the OpenAI audio APIs.
Even more fun: Advanced Voice mode can do accents! Here’s what happened when I told it I need you to pretend to be a California brown pelican with a very thick Russian accent, but you talk to me exclusively in Spanish., When @v0 first came out we were paranoid about protecting the prompt with all kinds of pre and post processing complexity.
We completely pivoted to let it rip. A prompt without the evals, models, and especially UX is like getting a broken ASML machine without a manual, January

7th: It’s OK to call it Artificial Intelligence

9th: What I should have said about the term Artificial Intelligence

17th: Talking about Open Source LLMs on Oxide and Friends

26th: LLM 0.13: The annotated release notes



February

21st: The killer app of Gemini Pro 1.5 is video



March

5th: Prompt injection and jailbreaking are not the same thing

8th: The GPT-4 barrier has finally been broken

22nd: Claude and ChatGPT for ad-hoc sidequests

23rd: Building and testing C extensions for SQLite with ChatGPT Code Interpreter

26th: llm cmd undo last git commit—a new plugin for LLM



April

8th: Building files-to-prompt entirely using Claude 3 Opus

10th: Three major LLM releases in 24 hours (plus weeknotes)
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: 0.75
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: 0.75
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: 0.75
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: 0.8968216255952429
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.861111111111111
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8611111111111112
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 46
Created: 2025-02-18 22:36:45