Model: s4um1l/legal-ft-84f7d2b4-c963-45b6-b749-04d2d76a110f
Author: s4um1l
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: What advantage do open models have over closed, hosted models according to the context?
sentences: On the other hand, as software engineers we are better placed to take advantage of this than anyone else. We’ve all been given weird coding interns—we can use our deep knowledge to prompt them to solve coding problems more effectively than anyone else can.
The ethics of this space remain diabolically complex
In September last year Andy Baio and I produced the first major story on the unlicensed training data behind Stable Diffusion.
Since then, almost every major LLM (and most of the image generation models) have also been trained on unlicensed data., There’s now a fascinating ecosystem of people training their own models on top of these foundations, publishing those models, building fine-tuning datasets and sharing those too.
The Hugging Face Open LLM Leaderboard is one place that tracks these. I can’t even attempt to count them, and any count would be out-of-date within a few hours.
The best overall openly licensed LLM at any time is rarely a foundation model: instead, it’s whichever fine-tuned community model has most recently discovered the best combination of fine-tuning data.
This is a huge advantage for open over closed models: the closed, hosted models don’t have thousands of researchers and hobbyists around the world collaborating and competing to improve them., 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: How is the user applying a similar interactive app concept in their Datasette project?
sentences: 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:, 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., 260 input tokens, 92 output tokens. Cost approximately 0.0024 cents (that’s less than a 400th of a cent).
This increase in efficiency and reduction in price is my single favourite trend from 2024. I want the utility of LLMs at a fraction of the energy cost and it looks like that’s what we’re getting.
Multimodal vision is common, audio and video are starting to emerge
My butterfly example above illustrates another key trend from 2024: the rise of multi-modal LLMs.
A year ago the single most notable example of these was GPT-4 Vision, released at OpenAI’s DevDay in November 2023. Google’s multi-modal Gemini 1.0 was announced on December 7th 2023 so it also (just) makes it into the 2023 window., source_sentence: What were the OpenAI pricing rates for GPT-4, GPT-4 Turbo, and GPT-35 Turbo in December 2023?
sentences: I run a bunch of them on my laptop. I run Mistral 7B (a surprisingly great model) on my iPhone. You can install several different apps to get your own, local, completely private LLM. My own LLM project provides a CLI tool for running an array of different models via plugins.
You can even run them entirely in your browser using WebAssembly and the latest Chrome!
Hobbyists can build their own fine-tuned models
I said earlier that building an LLM was still out of reach of hobbyists. That may be true for training from scratch, but fine-tuning one of those models is another matter entirely., Here’s the rest of the transcript. It’s bland and generic, but my phone can pitch bland and generic Christmas movies to Netflix now!
LLM prices crashed, thanks to competition and increased efficiency
The past twelve months have seen a dramatic collapse in the cost of running a prompt through the top tier hosted LLMs.
In December 2023 (here’s the Internet Archive for the OpenAI pricing page) OpenAI were charging $30/million input tokens for GPT-4, $10/mTok for the then-new GPT-4 Turbo and $1/mTok for GPT-3.5 Turbo., 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., source_sentence: According to the context, how many lines of Python code are generally needed to train a basic version of a powerful system?
sentences: The May 13th announcement of GPT-4o included a demo of a brand new voice mode, where the true multi-modal GPT-4o (the o is for “omni”) model could accept audio input and output incredibly realistic sounding speech without needing separate TTS or STT models.
The demo also sounded conspicuously similar to Scarlett Johansson... and after she complained the voice from the demo, Skye, never made it to a production product.
The delay in releasing the new voice mode after the initial demo caused quite a lot of confusion. I wrote about that in ChatGPT in “4o” mode is not running the new features yet., This remains astonishing to me. I thought a model with the capabilities and output quality of GPT-4 needed a datacenter class server with one or more $40,000+ GPUs.
These models take up enough of my 64GB of RAM that I don’t run them often—they don’t leave much room for anything else.
The fact that they run at all is a testament to the incredible training and inference performance gains that we’ve figured out over the past year. It turns out there was a lot of low-hanging fruit to be harvested in terms of model efficiency. I expect there’s still more to come., 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., source_sentence: What challenges does the author face when trying to evaluate multiple LLMs?
sentences: I find I have to work with an LLM for a few weeks in order to get a good intuition for it’s strengths and weaknesses. This greatly limits how many I can evaluate myself!
The most frustrating thing for me is at the level of individual prompting.
Sometimes I’ll tweak a prompt and capitalize some of the words in it, to emphasize that I really want it to OUTPUT VALID MARKDOWN or similar. Did capitalizing those words make a difference? I still don’t have a good methodology for figuring that out.
We’re left with what’s effectively Vibes Based Development. It’s vibes all the way down.
I’d love to see us move beyond vibes in 2024!
LLMs are really smart, and also really, really dumb, 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)., I think this means that, as individual users, we don’t need to feel any guilt at all for the energy consumed by the vast majority of our prompts. The impact is likely neglible compared to driving a car down the street or maybe even watching a video on YouTube.
Likewise, training. DeepSeek v3 training for less than $6m is a fantastic sign that training costs can and should continue to drop.
For less efficient models I find it useful to compare their energy usage to commercial flights. The largest Llama 3 model cost about the same as a single digit number of fully loaded passenger flights from New York to London. That’s certainly not nothing, but once trained that model can be used by millions of people at no extra training cost.
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: 0.9583333333333334
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: 0.9583333333333334
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: 0.9583333333333334
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: 0.9846220730654774
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9791666666666666
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9791666666666666
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 9
Created: 2025-04-29 23:25:33