barril
transformers
barril | transformers | |
---|---|---|
2 | 178 | |
36 | 125,741 | |
- | 2.0% | |
7.3 | 10.0 | |
16 days ago | 6 days ago | |
Python | Python | |
MIT License | 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.
barril
-
Pint: Makes Units Easy -Python
Internally the library has a "quantity type", which defines for a given quantity all the given conversions that are valid in the database and a "category" which is used so that you can create subsets of units which you want to consider valid for your application.
It's helpful in the context that you want to restrict what's valid for such a category (see: https://github.com/ESSS/barril/blob/master/src/barril/units/... for what that means -- think of defining a "cylinder width" as a subset of "length").
As a note, it's main use-case is NOT doing computations such as dividing/multiplying Scalars (albeit that's supported it does have some caveats -- so, while it also does dimensional analysis, it's really not the core functionality), rather the core functionality is making the conversions based on what's available in the unit database (so, you're not supposed to be creating units out of the unit database, rather, you want to collect input from the units you support and then make your conversions to other units you still support -- think of doing validation of input, converting to values in units expected by the user, defining a unit-system for input, converting to your internal unit-system for actual computation in C/C++, ...).
So, I guess it depends on what you consider as core for a units library. As I mentioned, it's created for an Oil & Gas use-case -- albeit it's also used in other engineering-related projects -- where the units are all pretty much well mapped in the application and you're worried that you are getting into units you're not expecting and that'd actually be an error.
p.s.: if you have specific use-cases which don't work as you expect, I suggest contacting the library maintainers -- once that was me, but it's been a while ;)
p.s.: thanks for letting me know about how to deal with dimensionless units in pint (I wasn't aware of that).
transformers
-
XLSTM: Extended Long Short-Term Memory
Fascinating work, very promising.
Can you summarise how the model in your paper differs from this one ?
https://github.com/huggingface/transformers/issues/27011
-
AI enthusiasm #9 - A multilingual chatbot📣🈸
transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)
-
Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
-
Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722
I don't think it would be that hard to switch it out for a pretrained ngram model.
-
AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
-
Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
-
Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
- HuggingFace Transformers: Qwen2
- HuggingFace Transformers Release v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2
- HuggingFace: Support for the Mixtral Moe