fast-soft-sort
transformers
| fast-soft-sort | transformers | |
|---|---|---|
| 1 | 178 | |
| 546 | 125,741 | |
| 0.7% | 2.0% | |
| 1.8 | 10.0 | |
| 3 months ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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fast-soft-sort
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[P] Torchsort - Fast, differentiable sorting and ranking in PyTorch
The original implementation (https://github.com/google-research/fast-soft-sort) uses numba for the forward pass and pure python for the backwards pass, while Torchsort has both implemented in C++/CUDA with additional parallelization over the batch dimension. You can find some benchmarks in the Torchsort readme.
transformers
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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
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AI enthusiasm #9 - A multilingual chatbot📣🈸
transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)
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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
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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.
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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 ❤️
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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
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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
What are some alternatives?
google-research - Google Research
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
torchsort - Fast, differentiable sorting and ranking in PyTorch
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
llama - Inference code for Llama models
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
ivy - The Unified AI Framework
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
huggingface_hub - The official Python client for the Huggingface Hub.
OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch