Model: szybe/legal-ft-b7a94f9f-5de7-476a-baa7-a2e80c3f10c1
Author: szybe
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: When did Google release their gemini-20-flash-thinking-exp model?
sentences: Your browser does not support the audio element.

OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also accepts audio input, and the Google Gemini apps can speak in a similar way to ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s meant to roll out in Q1 of 2025.
Google’s NotebookLM, released in September, took audio output to a new level by producing spookily realistic conversations between two “podcast hosts” about anything you fed into their tool. They later added custom instructions, so naturally I turned them into pelicans:


Your browser does not support the audio element., 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., The earliest of those was Google’s Gemini 1.5 Pro, released in February. In addition to producing GPT-4 level outputs, it introduced several brand new capabilities to the field—most notably its 1 million (and then later 2 million) token input context length, and the ability to input video.
I wrote about this at the time in The killer app of Gemini Pro 1.5 is video, which earned me a short appearance as a talking head in the Google I/O opening keynote in May., source_sentence: Which organizations currently have models that score higher than GPT-4-0314 on the Chatbot Arena leaderboard?
sentences: blogging
            105


            ai
            1250


            generative-ai
            1077


            llms
            1065

Next: Tom Scott, and the formidable power of escalating streaks
Previous: Last weeknotes of 2023


 
 


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2025, Then there’s the rest. If you browse the Chatbot Arena leaderboard today—still the most useful single place to get a vibes-based evaluation of models—you’ll see that GPT-4-0314 has fallen to around 70th place. The 18 organizations with higher scoring models are Google, OpenAI, Alibaba, Anthropic, Meta, Reka AI, 01 AI, Amazon, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Zhipu AI, xAI, AI21 Labs, Princeton and Tencent.
Training a GPT-4 beating model was a huge deal in 2023. In 2024 it’s an achievement that isn’t even particularly notable, though I personally still celebrate any time a new organization joins that list.
Some of those GPT-4 models run on my laptop, 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., source_sentence: Why is gullibility considered the biggest unsolved problem in the context of large language models?
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., Large Language Models
They’re actually quite easy to build
You can run LLMs on your own devices
Hobbyists can build their own fine-tuned models
We don’t yet know how to build GPT-4
Vibes Based Development
LLMs are really smart, and also really, really dumb
Gullibility is the biggest unsolved problem
Code may be the best application
The ethics of this space remain diabolically complex
My blog in 2023, One way to think about these models is an extension of the chain-of-thought prompting trick, first explored in the May 2022 paper Large Language Models are Zero-Shot Reasoners.
This is that trick where, if you get a model to talk out loud about a problem it’s solving, you often get a result which the model would not have achieved otherwise.
o1 takes this process and further bakes it into the model itself. The details are somewhat obfuscated: o1 models spend “reasoning tokens” thinking through the problem that are not directly visible to the user (though the ChatGPT UI shows a summary of them), then outputs a final result., source_sentence: Which other AI-related articles were posted around the same time as the "Industry’s Tardy Response" article according to the provided context?
sentences: We already knew LLMs were spookily good at writing code. If you prompt them right, it turns out they can build you a full interactive application using HTML, CSS and JavaScript (and tools like React if you wire up some extra supporting build mechanisms)—often in a single prompt.
Anthropic kicked this idea into high gear when they released Claude Artifacts, a groundbreaking new feature that was initially slightly lost in the noise due to being described half way through their announcement of the incredible Claude 3.5 Sonnet.
With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface.
Here’s my Extract URLs app, entirely generated by Claude:, 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., 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: Why does the author find it astonishing that models like GPT-4 can run on their current hardware?
sentences: 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., 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., 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.
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: 0.9637887397321441
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9513888888888888
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9513888888888888
name: Cosine Map@100
verified: no
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Created: 2025-04-30 00:17:07