Model: Ahmednogood/legal-ft-fa861294-da94-4f80-8f7f-e97b47c99faf
Author: Ahmednogood
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 unexpected behavior do LLMs sometimes exhibit that surprises even their trainers?
sentences: 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., On the one hand, we keep on finding new things that LLMs can do that we didn’t expect—and that the people who trained the models didn’t expect either. That’s usually really fun!
But on the other hand, the things you sometimes have to do to get the models to behave are often incredibly dumb.
Does ChatGPT get lazy in December, because its hidden system prompt includes the current date and its training data shows that people provide less useful answers coming up to the holidays?
The honest answer is “maybe”! No-one is entirely sure, but if you give it a different date its answers may skew slightly longer., 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 command was used to generate descriptions for the images in the given 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, Stuff we figured out about AI in 2023





















Simon Willison’s Weblog
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Stuff we figured out about AI in 2023
31st December 2023
2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.
Here’s my attempt to round up the highlights in one place!, source_sentence: Which three AI models were freely available to most of the world for a few months this year?
sentences: Still, I’m surprised that no-one has beaten the now almost year old GPT-4 by now. OpenAI clearly have some substantial tricks that they haven’t shared yet.
Vibes Based Development
As a computer scientist and software engineer, LLMs are infuriating.
Even the openly licensed ones are still the world’s most convoluted black boxes. We continue to have very little idea what they can do, how exactly they work and how best to control them.
I’m used to programming where the computer does exactly what I tell it to do. Prompting an LLM is decidedly not that!
The worst part is the challenge of evaluating them.
There are plenty of benchmarks, but no benchmark is going to tell you if an LLM actually “feels” right when you try it for a given task., This prompt-driven custom interface feature is so powerful and easy to build (once you’ve figured out the gnarly details of browser sandboxing) that I expect it to show up as a feature in a wide range of products in 2025.
Universal access to the best models lasted for just a few short months
For a few short months this year all three of the best available models—GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely available to most of the world., So training an LLM still isn’t something a hobbyist can afford, but it’s no longer the sole domain of the super-rich. I like to compare the difficulty of training an LLM to that of building a suspension bridge—not trivial, but hundreds of countries around the world have figured out how to do it. (Correction: Wikipedia’s Suspension bridges by country category lists 44 countries).
You can run LLMs on your own devices
In January of this year, I thought it would be years before I could run a useful LLM on my own computer. GPT-3 and 3.5 were pretty much the only games in town, and I thought that even if the model weights were available it would take a $10,000+ server to run them., source_sentence: What changes have occurred in the energy usage and environmental impact of running AI prompts in recent years?
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., 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)., Those US export regulations on GPUs to China seem to have inspired some very effective training optimizations!
The environmental impact got better
A welcome result of the increased efficiency of the models—both the hosted ones and the ones I can run locally—is that the energy usage and environmental impact of running a prompt has dropped enormously over the past couple of years.
OpenAI themselves are charging 100x less for a prompt compared to the GPT-3 days. I have it on good authority that neither Google Gemini nor Amazon Nova (two of the least expensive model providers) are running prompts at a loss., source_sentence: How does the author describe Apple's current LLM features compared to frontier LLM capabilities?
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., 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., Those US export regulations on GPUs to China seem to have inspired some very effective training optimizations!
The environmental impact got better
A welcome result of the increased efficiency of the models—both the hosted ones and the ones I can run locally—is that the energy usage and environmental impact of running a prompt has dropped enormously over the past couple of years.
OpenAI themselves are charging 100x less for a prompt compared to the GPT-3 days. I have it on good authority that neither Google Gemini nor Amazon Nova (two of the least expensive model providers) are running prompts at a loss.
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: 0.9166666666666666
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: 7
Created: 2025-05-05 18:22:41