Model: manmah/legal-ft-717cb2ad-5d19-4d52-ad34-5656c2895fa9
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 challenge related to prompt injection has seen little progress since September 2022?
sentences: 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?, 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):, Industry’s Tardy Response to the AI Prompt Injection Vulnerability on RedMonk Conversations


Posted 31st December 2023 at 11:59 pm · Follow me on Mastodon, Bluesky, Twitter or subscribe to my newsletter


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This is Stuff we figured out about AI in 2023 by Simon Willison, posted on 31st December 2023.

Part of series LLMs annual review

Stuff we figured out about AI in 2023 - Dec. 31, 2023, 11:59 p.m. 
Things we learned about LLMs in 2024 - Dec. 31, 2024, 6:07 p.m., source_sentence: Which company released the QwQ model under an Apache 20 license?
sentences: I also gave a bunch of talks and podcast appearances. I’ve started habitually turning my talks into annotated presentations—here are my best from 2023:

Prompt injection explained, with video, slides, and a transcript
Catching up on the weird world of LLMs
Making Large Language Models work for you
Open questions for AI engineering
Embeddings: What they are and why they matter
Financial sustainability for open source projects at GitHub Universe

And in podcasts:


What AI can do for you on the Theory of Change

Working in public on Path to Citus Con

LLMs break the internet on the Changelog

Talking Large Language Models on Rooftop Ruby

Thoughts on the OpenAI board situation on Newsroom Robots, OpenAI are not the only game in town here. Google released their first entrant in the category, gemini-2.0-flash-thinking-exp, on December 19th.
Alibaba’s Qwen team released their QwQ model on November 28th—under an Apache 2.0 license, and that one I could run on my own machine. They followed that up with a vision reasoning model called QvQ on December 24th, which I also ran locally.
DeepSeek made their DeepSeek-R1-Lite-Preview model available to try out through their chat interface on November 20th.
To understand more about inference scaling I recommend Is AI progress slowing down? by Arvind Narayanan and Sayash Kapoor., “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., source_sentence: How has Apple’s MLX library impacted the performance of running machine learning models on Apple Silicon?
sentences: 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., 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., 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., source_sentence: What are some ways to run local, private large language models (LLMs) mentioned in the context?
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’m still trying to figure out the best patterns for doing this for my own work. Everyone knows that evals are important, but there remains a lack of great guidance for how to best implement them—I’m tracking this under my evals tag. My SVG pelican riding a bicycle benchmark is a pale imitation of what a real eval suite should look like.
Apple Intelligence is bad, Apple’s MLX library is excellent
As a Mac user I’ve been feeling a lot better about my choice of platform this year.
Last year it felt like my lack of a Linux/Windows  machine with an NVIDIA GPU was a huge disadvantage in terms of trying out new models., 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., source_sentence: What is the most important factor in determining the quality of a trained model according to the context?
sentences: 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., Now add a walrus: Prompt engineering in DALL-E 3
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Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive
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ChatGPT can’t access the internet, even though it really looks like it can
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Stanford Alpaca, and the acceleration of on-device large language model development
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Run Llama 2 on your own Mac using LLM and Homebrew
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Midjourney 5.1
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Think of language models like ChatGPT as a “calculator for words”
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Multi-modal prompt injection image attacks against GPT-4V
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27.4k, I think people who complain that LLM improvement has slowed are often missing the enormous advances in these multi-modal models. Being able to run prompts against images (and audio and video) is a fascinating new way to apply these models.
Voice and live camera mode are science fiction come to life
The audio and live video modes that have started to emerge deserve a special mention.
The ability to talk to ChatGPT first arrived in September 2023, but it was mostly an illusion: OpenAI used their excellent Whisper speech-to-text model and a new text-to-speech model (creatively named tts-1) to enable conversations with the ChatGPT mobile apps, but the actual model just saw text.
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: 6
Created: 2025-04-30 01:10:50