Model: manmah/legal-ft-2aefb51e-1a19-43c1-a5ff-7d28d65534da
Author: manmah
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 was the typical context length accepted by most models last year?
sentences: 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):, Gemini 1.5 Pro also illustrated one of the key themes of 2024: increased context lengths. Last year most models accepted 4,096 or 8,192 tokens, with the notable exception of Claude 2.1 which accepted 200,000. Today every serious provider has a 100,000+ token model, and Google’s Gemini series accepts up to 2 million., 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., source_sentence: What challenges does the author face when trying to evaluate multiple LLMs?
sentences: We don’t yet know how to build GPT-4
Frustratingly, despite the enormous leaps ahead we’ve had this year, we are yet to see an alternative model that’s better than GPT-4.
OpenAI released GPT-4 in March, though it later turned out we had a sneak peak of it in February when Microsoft used it as part of the new Bing.
This may well change in the next few weeks: Google’s Gemini Ultra has big claims, but isn’t yet available for us to try out.
The team behind Mistral are working to beat GPT-4 as well, and their track record is already extremely strong considering their first public model only came out in September, and they’ve released two significant improvements since then., 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, Except... you can run generated code to see if it’s correct. And with patterns like ChatGPT Code Interpreter the LLM can execute the code itself, process the error message, then rewrite it and keep trying until it works!
So hallucination is a much lesser problem for code generation than for anything else. If only we had the equivalent of Code Interpreter for fact-checking natural language!
How should we feel about this as software engineers?
On the one hand, this feels like a threat: who needs a programmer if ChatGPT can write code for you?, source_sentence: What are some ways mentioned to run local, private large language models (LLMs) on personal devices?
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., 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., 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):, source_sentence: How has the value of prompt-driven app generation changed from 2023 to 2024?
sentences: 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., The environmental impact got much, much worse
The much bigger problem here is the enormous competitive buildout of the infrastructure that is imagined to be necessary for these models in the future.
Companies like Google, Meta, Microsoft and Amazon are all spending billions of dollars rolling out new datacenters, with a very material impact on the electricity grid and the environment. There’s even talk of spinning up new nuclear power stations, but those can take decades.
Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued crash in LLM prices might hint that it’s not. But would you want to be the big tech executive that argued NOT to build out this infrastructure only to be proven wrong in a few years’ time?, 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 makes the prompt-driven custom interface feature powerful and easy to build despite the challenges of browser sandboxing?
sentences: 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., The environmental impact got much, much worse
The much bigger problem here is the enormous competitive buildout of the infrastructure that is imagined to be necessary for these models in the future.
Companies like Google, Meta, Microsoft and Amazon are all spending billions of dollars rolling out new datacenters, with a very material impact on the electricity grid and the environment. There’s even talk of spinning up new nuclear power stations, but those can take decades.
Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued crash in LLM prices might hint that it’s not. But would you want to be the big tech executive that argued NOT to build out this infrastructure only to be proven wrong in a few years’ time?, We don’t yet know how to build GPT-4
Frustratingly, despite the enormous leaps ahead we’ve had this year, we are yet to see an alternative model that’s better than GPT-4.
OpenAI released GPT-4 in March, though it later turned out we had a sneak peak of it in February when Microsoft used it as part of the new Bing.
This may well change in the next few weeks: Google’s Gemini Ultra has big claims, but isn’t yet available for us to try out.
The team behind Mistral are working to beat GPT-4 as well, and their track record is already extremely strong considering their first public model only came out in September, and they’ve released two significant improvements since then.
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: 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: 0.875
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: 0.875
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: 0.9538662191964322
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9375
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9375
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 37
Created: 2025-05-05 05:22:53