loda-rust
hivemind
| loda-rust | hivemind | |
|---|---|---|
| 5 | 40 | |
| 23 | 1,842 | |
| - | 1.7% | |
| 9.9 | 5.4 | |
| 3 months ago | about 2 months ago | |
| Rust | Python | |
| Apache License 2.0 | MIT License |
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.
loda-rust
- SETI Home Is in Hibernation
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Understanding Bloom Filters by building one
https://github.com/loda-lang/loda-rust/blob/develop/rust_pro...
I use 4 bloomfilters for rejecting programs that aren't present in the oeis database. First 10 terms are computed, and reject if the terms aren't present in the 1st bloomfilter. Next 10 terms more are computed, and rejected if the 20 terms aren't present in the 2nd bloomfilter. All the way up to 40 terms. If a mined program has possible 40 correct terms, there is a chance that the program may compute the entire oeis integer sequence correct.
Shameless self promotion: If you have cpu resources to spare, then please contribute to LODA, an open source project for mining oeis integer sequences.
- Ask HN: What web apps use WASM today?
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Paul Graham's Twitter thread on Search engines and SEO spam
https://github.com/loda-lang/loda-rust/blob/develop/script/t...
Example of the 100 most similar documents:
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Loda-lang – language, computational model, and OEIS miner
https://github.com/loda-lang/loda-rust/blob/develop/document...
OEIS has a number of references from other sequences. If it's a popular sequence then the number of references is high. Usually it's humans that specify the references.
Unlike OEIS, the LODA programs references are mined. The most popular LODA program (A132106) has only a few references in OEIS, maybe it's overlooked in OEIS.
hivemind
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You can now train a 70B language model at home
https://github.com/learning-at-home/hivemind is also relevant
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Would anyone be interested in contributing to some group projects?
I really hope you'll join me, for the Petals support, at least! A single docker-compose.yml file is all we need, for now. If we are able to find enough people willing to host some smaller models, perhaps we could expand into the Hivemind, and create our own, custom foundation model one day?
- Hive mind:Train deep learning models on thousands of volunteers across the world
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Could a model not be trained by a decentralized network? Like Seti @ home or kinda-sorta like bitcoin. Petals accomplishes this somewhat, but if raw computer power is the only barrier to open-source I'd be happy to try organizing decentalized computing efforts
Decentralized deep learning: https://github.com/learning-at-home/hivemind
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Orca (built on llama13b) looks like the new sheriff in town
https://github.com/learning-at-home/hivemind - same people behind it, was made before petals I think.
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Do you think that AI research will slow down to a halt because of regulation?
not if we rise to meet that challenge. here's a few tools that facilitate AI research in the face of an advanced persistent threat: Hivemind- a distributed Pytorch framework
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LLM@home
yeah, there's Hivemind. and there's research wrt how to chunk out training workload so it can be scaled up. not sure why there's commentary that latency issues would limit this sort of enterprise, the architecture typically isn't designed for liveness. other subfields of distributed training/inference include zero-knowledge machine learning. besides all of that, there's also adversarial computation like SafetyNets and refereed delegation of computation.
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[D] Google "We Have No Moat, And Neither Does OpenAI": Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI
We already have the software for it. There are some projects, but the one I'm most familiar with is https://github.com/learning-at-home/hivemind for training and it's sister project https://petals.ml/ for running large models distributed.
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Run 100B+ language models at home, BitTorrent‑style
I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1].
0: https://github.com/learning-at-home/hivemind
1: https://arxiv.org/abs/1912.04977
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SETI Home Is in Hibernation
The Hivemind project is just that
https://github.com/learning-at-home/hivemind
What are some alternatives?
wasm4 - Build retro games using WebAssembly for a fantasy console.
replika-research - Replika.ai Research Papers, Posters, Slides & Datasets
python-bloom-filter - Bloom filter for Python
alpa - Training and serving large-scale neural networks with auto parallelization.
uBlock-Origin-dev-filter - Filters to block and remove copycat-websites from DuckDuckGo, Google and other search engines. Specific to dev websites like StackOverflow or GitHub.
Super-SloMo - PyTorch implementation of Super SloMo by Jiang et al.
loda - LODA is an assembly language, a computational model and a tool for mining integer sequence programs.
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
loda-cpp - Runtime and miner for the LODA language written in C++
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
textyle - Web tilemap editor for 2D games built using React JS, WebGL and Rust (WASM).
HiveMind-core - Join the OVOS collective, utils for OpenVoiceOS mesh networking