rmi VS google-research

Compare rmi vs google-research and see what are their differences.

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rmi google-research
1 98
52 33,452
- 1.0%
0.0 9.6
over 3 years ago 5 days ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 Apache License 2.0
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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.

rmi

Posts with mentions or reviews of rmi. We have used some of these posts to build our list of alternatives and similar projects.

google-research

Posts with mentions or reviews of google-research. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-10.
  • Show HN: Next-token prediction in JavaScript – build fast LLMs from scratch
    11 projects | news.ycombinator.com | 10 Apr 2024
    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
    1 project | news.ycombinator.com | 5 Apr 2024
  • Jpegli: A New JPEG Coding Library
    9 projects | news.ycombinator.com | 3 Apr 2024
    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
    1 project | news.ycombinator.com | 31 Jan 2024
  • Vector Databases: A Technical Primer [pdf]
    7 projects | news.ycombinator.com | 12 Jan 2024
    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...

  • Labs.Google
    1 project | news.ycombinator.com | 22 Dec 2023
    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.
  • Smerf: Streamable Memory Efficient Radiance Fields
    3 projects | news.ycombinator.com | 13 Dec 2023
    https://github.com/google-research/google-research/blob/mast...
  • Shisa 7B: a new JA/EN bilingual model based on Mistral 7B
    2 projects | /r/LocalLLaMA | 7 Dec 2023
    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.
  • Translate to and from 400+ languages locally with MADLAD-400
    1 project | /r/LocalLLaMA | 10 Nov 2023
    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
    2 projects | dev.to | 8 Oct 2023

What are some alternatives?

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Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.

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.

fastai - The fastai deep learning library

Milvus - A cloud-native vector database, storage for next generation AI applications

RadixSpline - A Single-Pass Learned Index

bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
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SaaSHub - Software Alternatives and Reviews
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