SpecBAS
An enhanced Sinclair BASIC interpreter for modern PCs (by ZXDunny)
x-transformers
A simple but complete full-attention transformer with a set of promising experimental features from various papers (by lucidrains)
SpecBAS | x-transformers | |
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4 | 10 | |
145 | 4,210 | |
- | - | |
7.0 | 8.7 | |
about 2 months ago | 11 days ago | |
Pascal | Python | |
GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
SpecBAS
Posts with mentions or reviews of SpecBAS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-09.
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New to BASIC
You could give something like SpecBAS a go: https://github.com/ZXDunny/SpecBAS
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Hacker News top posts: May 9, 2021
SpecBAS: An enhanced Sinclair BASIC interpreter for modern PCs\ (3 comments)
- SpecBAS: An enhanced Sinclair BASIC interpreter for modern PCs
- Old School Style Basic Interpreters With Graphics
x-transformers
Posts with mentions or reviews of x-transformers.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-26.
- x-transformers
- GPT-4 architecture: what we can deduce from research literature
- Doubt about transformers
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The GPT Architecture, on a Napkin
it is all documented here, in writing and in code https://github.com/lucidrains/x-transformers
you will want to use rotary embeddings, if you do not need length extrapolation
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[R] Deepmind's Gato: a generalist learning agent
it is just a single transformer encoder, so just use https://github.com/lucidrains/x-transformers with ff_glu set to True
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[D] Transformer sequence generation - is it truly quadratic scaling?
However, I've come across the concept of Key, Value Caching in Transformer-Decoders recently (e.g. Figure 3 here), wherein because each output (and hence each input, since the model is autoregressive) only depends on previous outputs (inputs), we don't need to re-compute Key and Value vectors for all t < t_i at timestep i of the sequence. My intuition leads me to believe, then, that (unconditioned) inference for a decoder-only model uses an effective sequence length of 1 (the most recently produced token is the only real input that requires computation on), making Attention a linear-complexity operation. This thinking seems to be validated by this github issue, and this paper (2nd paragraph of Introduction).
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[D] Sudden drop in loss after hours of no improvement - is this a thing?
The Project - Model: The primary architecture consists of a CNN with a transformer encoder and decoder. At first, I used my implementation of self-attention. Still, due to it not converging, I switched to using x-transformer implementation by lucidrains - as it includes improvements from many papers. The objective is simple; the CNN encoder converts images to a high-level representation; feeds them to the transformer encoder for information flow. Finally, a transformer decoder tries to decode the text character-by-character using autoregressive loss. After two weeks of trying around different things, the training did not converge within the first hour - as this is the usual mark I use to validate if a model is learning or not.
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Hacker News top posts: May 9, 2021
X-Transformers: A fully-featured transformer with experimental features\ (25 comments)
- X-Transformers: A fully-featured transformer with experimental features
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[D] Theoretical papers on transformers? (or attention mechanism, or just seq2seq?)
One thing I’ve looked at is the fact that there’s no obvious reason to distinguish between W_K and W_Q in the formulation of a transformer as far as I can tell. However if you build a transformer where you merge the two matrices, it doesn’t learn as well. It still learns, but not as well. You can try out the code here. The training loss can be seen here, though we aborted the run because of how poorly it was doing.
What are some alternatives?
When comparing SpecBAS and x-transformers you can also consider the following projects:
crawl - Dungeon Crawl: Stone Soup official repository
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.