You are right that the concept of "safe" is nebulous, but the goal here is specifically to be XSS-safe [1]. Elements or properties that could allow scripts to execute are removed. This functionality lives in the user agent and prevents adding unsafe elements to the DOM itself, so it should be easier to get correct than a string-to-string sanitizer. The logic of "is the element currently being added to the DOM a <script>" is fundamentally easier to get right than "does this HTML string include a script tag".
The languagemodels[1] package that I maintain might meet your needs.
My primary use case is education, as myself and others use this for short student projects[2] related to LLMs, but there's nothing preventing this package from being used in other ways. It includes a basic in-process vector store[3].
It would be nice to see the Phind Instant weights released under a permissive license. It looks like it could be a useful tool in the local-only code model toolbox.
The speedup would not be that high in practice for folks already using speculative decoding[1]. ANPD is similar but uses a simpler and faster drafting approach. These two enhancements can't be meaningfully stacked. Here's how the paper describes it:
> ANPD dynamically generates draft outputs via an adaptive N-gram module using real-time statistics, after which the drafts are verified by the LLM. This characteristic is exactly the difference between ANPD and the previous speculative decoding methods.
ANPD does provide a more general-purpose solution to drafting that does not require training, loading, and running draft LLMs.
You might be interested in "Text Embeddings Reveal (Almost) As Much As Text":
> We train our model to decode text embeddings from two state-of-the-art embedding models, and also show that our model can recover important personal information (full names) from a dataset of clinical notes.
This included full model weights along with a detailed description of the dataset, training process, and ablations that led them to that architecture. T5 was state-of-the-art on many benchmarks when it was released, but it was of course quickly eclipsed by GPT-3.
It was common practice from Google (BERT, T5), Meta (BART), OpenAI (GPT1, GPT2) and others to release full training details and model weights. Following GPT-3, it became much more common for labs to not release full details or model weights.
> PNG uses deflate. General byte-level patterns. It does not do bespoke image-specific stuff.
That's not quite the whole story. PNG does include simple filters to represent a line as a difference from the line above, and that may be what the original post is referring to. [1]
There is a large amount of theoretical research on the subject of energy limits in computing. For example, Landauer's principle states any irreversible change in information requires some amount of dissipated heat, and therefore some energy input [1].
Reversible computing is an attempt to get around this limit by removing irreversible state changes [2].
This is great to see. It looks like the size of the embedding vector is half the size of text-embedding-ada-002 (768 vs 1536) while providing competitive performance. This will save space in databases and make lookups somewhat faster.
For those unaware, if 512 tokens of context is sufficient for your use case, there are already many options that outperform text-embedding-ada-002 on common benchmarks:
> In this work, we introduce TinyStories, a synthetic dataset of short stories that only contain words that a typical 3 to 4-year-olds usually understand, generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 10 million total parameters), or have much simpler architectures (with only one transformer block), yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities.
Thanks for pointing that out. Classification is half-baked at the moment. It should ultimately be restricting output to only appropriate labels, but right now it is simply sampling.
You can actually get these models to do this, but you have to ask:
>>> lm.do(f"Answer from the context: What is YCombinator? {lm.get_wiki('Python')}")
'The context does not provide information about YCombinator.'
>>> lm.do(f"Answer from the context: What is YCombinator? {lm.get_wiki('YCombinator')}")
'YCombinator is an American technology startup accelerator that has launched over 4,000 companies, including Airbnb, Coinbase, Cruise, DoorDash, Dropbox, Instacart, Quora, PagerDuty, Reddit, Stripe and Twitch.'
Without being told to be grounded, the model will guess. However, it may be able to identify information not available in a provided context.
One of my goals for this package is to provide a way for folks to learn about the basics of grounding and semantic search.
I appreciated Richard's take on AI-assisted programming:
> It doesn’t remove the need to communicate precisely. ... In a sense, that’s almost the definition of what makes a programming language a programming language, as opposed to some other kind of language. There’s a precise semantics to everything that is said in that language.
> With the advent of AI-assisted programming, now we have sort of a new method of communication in that it’s a communication from computer back to human. In that, you might have something like ChatGPT producing the code, but a human still has to read that code and make sure that it does what you think it does. And as a medium of precise communication, it’s still very important to have a programming language that allows that communication to happen.
[1] https://developer.mozilla.org/en-US/docs/Web/API/Element/set...