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].
> Implement when.js as a simple, zero-dependency js library following SPEC.md exactly.
https://github.com/jncraton/whenwords/pulls