weggli
google-research
weggli | google-research | |
---|---|---|
13 | 98 | |
2,270 | 32,863 | |
0.7% | 0.9% | |
3.3 | 9.6 | |
3 months ago | 4 days ago | |
Rust | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
weggli
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Jpegli: A New JPEG Coding Library
JPEGLI = A small JPEG
The suffix -li is used in Swiss German dialects. It forms a diminutive of the root word, by adding -li to the end of the root word to convey the smallness of the object and to convey a sense of intimacy or endearment.
This obviously comes out of Google Zürich.
Other notable Google projects using Swiss German:
https://github.com/google/gipfeli high-speed compression
Gipfeli = Croissant
https://github.com/google/guetzli perceptual JPEG encoder
Guetzli = Cookie
https://github.com/weggli-rs/weggli semantic search tool
Weggli = Bread roll
https://github.com/google/brotli lossless compression
Brötli = Small bread
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Meet ast-grep: a Rust-based tool for code searching, linting, rewriting using AST
I use weggli quite frequently for security analysis. Are there any plans for friendly competition in terms of equivalent features (not:, subexpressions, etc.).
- Ask HN: Any other tools like CodeQL?
- Coding Style -agnostic search for C++
- GitHub - googleprojectzero/weggli: weggli is a fast and robust semantic search tool for C and C++ codebases. It is designed to help security researchers identify interesting functionality in large codebases.
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fccf: A command-line tool that quickly searches through C/C++ source code in a directory based on a search string and prints relevant code snippets that match the query
Is this similar to weggli? https://github.com/googleprojectzero/weggli
- weggli - fast and robust semantic search tool for C and C++ codebases
- weggli – fast and robust semantic search tool for C and C++ codebases
- googleprojectzero/weggli: weggli is a fast and robust semantic search tool for C and C++ codebases. It is designed to help security researchers identify interesting functionality in large codebases.
- weggli: fast and robust semantic search tool for C and C++ codebases
google-research
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Show HN: Next-token prediction in JavaScript – build fast LLMs from scratch
People on here will be happy to say that I do a similar thing, however my sequence length is dynamic because I also use a 2nd data structure - I'll use pretentious academic speak: I use a simple bigram LM (2-gram) for single next-word likeliness and separately a trie that models all words and phrases (so, n-gram). Not sure how many total nodes because sentence lengths vary in training data, but there are about 200,000 entry points (keys) so probably about 2-10 million total nodes in the default setup.
"Constructing 7-gram LM": They likely started with bigrams (what I use) which only tells you the next word based on 1 word given, and thought to increase accuracy by modeling out more words in a sequence, and eventually let the user (developer) pass in any amount they want to model (https://github.com/google-research/google-research/blob/5c87...). I thought of this too at first, but I actually got more accuracy (and speed) out of just keeping them as bigrams and making a totally separate structure that models out an n-gram of all phrases (e.g. could be a 24-token long sequence or 100+ tokens etc. I model it all) and if that phrase is found, then I just get the bigram assumption of the last token of the phrase. This works better when the training data is more diverse (for a very generic model), but theirs would probably outperform mine on accuracy when the training data has a lot of nearly identical sentences that only change wildly toward the end - I don't find this pattern in typical data though, maybe for certain coding and other tasks there are those patterns though. But because it's not dynamic and they make you provide that number, even a low number (any phrase longer than 2 words) - theirs will always have to do more lookup work than with simple bigrams and they're also limited by that fixed number as far as accuracy. I wonder how scalable that is - if I need to train on occasional ~100-word long sentences but also (and mostly) just ~3-word long sentences, I guess I set this to 100 and have a mostly "undefined" trie.
I also thought of the name "LMJS", theirs is "jslm" :) but I went with simply "next-token-prediction" because that's what it ultimately does as a library. I don't know what theirs is really designed for other than proving a concept. Most of their code files are actually comments and hypothetical scenarios.
I recently added a browser example showing simple autocomplete using my library: https://github.com/bennyschmidt/next-token-prediction/tree/m... (video)
And next I'm implementing 8-dimensional embeddings that are converted to normalized vectors between 0-1 to see if doing math on them does anything useful beyond similarity, right now they look like this:
[nextFrequency, prevalence, specificity, length, firstLetter, lastLetter, firstVowel, lastVowel]
- Google Research website is down
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Jpegli: A New JPEG Coding Library
The change was literally just made: https://github.com/google-research/google-research/commit/4a...
It appears this was in response to Hacker News comments.
- Multi-bitrate JPEG compression perceptual evaluation dataset 2023
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Vector Databases: A Technical Primer [pdf]
There are options such as Google's ScaNN that may let you go farther before needing to consider specialized databases.
https://github.com/google-research/google-research/blob/mast...
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Labs.Google
I feel it was unnecesary to create this because https://research.google/ already exists? It just seems like they want to take another URL with a "pure" domain name instead of psubdirectories, etc parts.
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Smerf: Streamable Memory Efficient Radiance Fields
https://github.com/google-research/google-research/blob/mast...
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Shisa 7B: a new JA/EN bilingual model based on Mistral 7B
You could also try some dedicated translation models like https://huggingface.co/facebook/nllb-moe-54b (or https://github.com/google-research/google-research/tree/master/madlad_400 for something smaller) and see how they do.
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Translate to and from 400+ languages locally with MADLAD-400
Google released T5X checkpoints for MADLAD-400 a couple of months ago, but nobody could figure out how to run them. Turns out the vocabulary was wrong, but they uploaded the correct one last week.
- Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples
What are some alternatives?
quick-lint-js - quick-lint-js finds bugs in JavaScript programs
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
locust - "git diff" over abstract syntax trees
fast-soft-sort - Fast Differentiable Sorting and Ranking
ripgrep - ripgrep recursively searches directories for a regex pattern while respecting your gitignore
faiss - A library for efficient similarity search and clustering of dense vectors.
fzf - :cherry_blossom: A command-line fuzzy finder
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Sourcetrail - Sourcetrail - free and open-source interactive source explorer
Milvus - A cloud-native vector database, storage for next generation AI applications
ast-grep - ⚡A CLI tool for code structural search, lint and rewriting. Written in Rust
struct2depth - Models and examples built with TensorFlow