Model: sepidnes/ai-blog-ft-d8908658-beeb-412f-ab0c-5bcb7cdb4fbd
Author: sepidnes
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:157, 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:157, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget: source_sentence: Why was the sequel to o1 named o3 instead of o2?
sentences: 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):, The biggest innovation here is that it opens up a new way to scale a model: instead of improving model performance purely through additional compute at training time, models can now take on harder problems by spending more compute on inference.
The sequel to o1, o3 (they skipped “o2” for European trademark reasons) was announced on 20th December with an impressive result against the ARC-AGI benchmark, albeit one that likely involved more than $1,000,000 of compute time expense!
o3 is expected to ship in January. I doubt many people have real-world problems that would benefit from that level of compute expenditure—I certainly don’t!—but it appears to be a genuine next step in LLM architecture for taking on much harder problems., 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 have LLMs impacted the author's personal productivity and entertainment?
sentences: A lot of people are excited about AI agents—an infuriatingly vague term that seems to be converging on “AI systems that can go away and act on your behalf”. We’ve been talking about them all year, but I’ve seen few if any examples of them running in production, despite lots of exciting prototypes.
I think this is because of gullibility.
Can we solve this? Honestly, I’m beginning to suspect that you can’t fully solve gullibility without achieving AGI. So it may be quite a while before those agent dreams can really start to come true!
Code may be the best application
Over the course of the year, it’s become increasingly clear that writing code is one of the things LLMs are most capable of., 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., 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., source_sentence: What problem does the author highlight about people who use the term “agents”?
sentences: These price drops are driven by two factors: increased competition and increased efficiency. The efficiency thing is really important for everyone who is concerned about the environmental impact of LLMs. These price drops tie directly to how much energy is being used for running prompts.
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?, “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., The GPT-4 barrier was comprehensively broken
Some of those GPT-4 models run on my laptop
LLM prices crashed, thanks to competition and increased efficiency
Multimodal vision is common, audio and video are starting to emerge
Voice and live camera mode are science fiction come to life
Prompt driven app generation is a commodity already
Universal access to the best models lasted for just a few short months
“Agents” still haven’t really happened yet
Evals really matter
Apple Intelligence is bad, Apple’s MLX library is excellent
The rise of inference-scaling “reasoning” models
Was the best currently available LLM trained in China for less than $6m?
The environmental impact got better
The environmental impact got much, much worse, source_sentence: What are some potential negative uses of Large Language Models as described in the context?
sentences: Against this photo of butterflies at the California Academy of Sciences:


A shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange slices of fruit are visible inside the dish.
Two butterflies are positioned in the feeder, one is a dark brown/black butterfly with white/cream-colored markings.  The other is a large, brown butterfly with patterns of lighter brown, beige, and black markings, including prominent eye spots. The larger brown butterfly appears to be feeding on the fruit., 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., 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 one major weakness of large language models (LLMs) mentioned in the context, especially in relation to generating code?
sentences: If you think about what they do, this isn’t such a big surprise. The grammar rules of programming languages like Python and JavaScript are massively less complicated than the grammar of Chinese, Spanish or English.
It’s still astonishing to me how effective they are though.
One of the great weaknesses of LLMs is their tendency to hallucinate—to imagine things that don’t correspond to reality. You would expect this to be a particularly bad problem for code—if an LLM hallucinates a method that doesn’t exist, the code should be useless., These price drops are driven by two factors: increased competition and increased efficiency. The efficiency thing is really important for everyone who is concerned about the environmental impact of LLMs. These price drops tie directly to how much energy is being used for running prompts.
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?, 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.
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: 0.875
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9583333333333334
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: 0.875
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3194444444444444
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: 0.875
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9583333333333334
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: 0.9455223360506796
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9270833333333334
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9270833333333334
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 8
Created: 2025-05-03 23:55:34