numpyro
trax
numpyro | trax | |
---|---|---|
2 | 7 | |
2,046 | 7,957 | |
1.1% | 0.4% | |
8.7 | 4.7 | |
about 9 hours ago | 3 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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numpyro
- Bayesian Analysis with Python
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Saving the World with Bayesian Modeling
Perhaps an alternative to look into: Numpyro [1] has a JAX backend so can be really fast when compiled; and it can run on GPUs. So that might be helpful for your problem with loads of data.
[1] https://github.com/pyro-ppl/numpyro
trax
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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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Replit's new Code LLM was trained in 1 week
and the implementation https://github.com/google/trax/blob/master/trax/models/resea... if you are interested.
Hope you get to look into this!
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RedPajama: Reproduction of Llama with Friendly License
Thank you for developing the pipeline and amassing considerable compute for gathering and preprocessing this dataset!
I'm not sure if this is the right place to ask about this, but could you consider training an LLM using a more advanced, sparse transformer architecture (specifically, "Terraformer" from this paper https://arxiv.org/abs/2111.12763 and this codebase https://github.com/google/trax/blob/master/trax/models/resea... by Google Brain and OpenAI)? I understand the pressure to focus on training a straightforward LLaMA replication, but of course you see that it's a legacy dense architecture which limits its inference performance. This new architecture is not just an academic curiosity but is already validated at scale by Google, providing 10x+ inference performance boost on the same hardware.
Frankly, the community's compute budget - for training and for inference - isn't infinite, and neither is the public's interest in models that do not have advantage (at least in convenience) over closed-source ones; and so we should utilize both those resources as efficiently as possible. It could be a big step forward if you trained at least LLaMA-Terraformer-7B and 13B foundation models on the whole dataset.
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The founder of Gmail claims that ChatGPT can “kill” Google in two years.
But a couple years later they came out with open source implementations yeah: https://github.com/google/trax/tree/master/trax/models/reformer
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[D] Paper Explained - Sparse is Enough in Scaling Transformers (aka Terraformer) | Video Walkthrough
Code: https://github.com/google/trax/blob/master/trax/examples/Terraformer_from_scratch.ipynb
- Why would I want to develop yet another deep learning framework?
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How to train large models on a normal laptop?
Training language models is expensive. Train the biggest model you can afford. I assume you've tried the colab from the reformer GitHub: https://github.com/google/trax/tree/master/trax/models/reformer
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
flax - Flax is a neural network library for JAX that is designed for flexibility.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
dm-haiku - JAX-based neural network library
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
muzero-general - MuZero
BayesianEcosystems_IAP - Notes and code for Bayesian ecosystem modeling IAP course
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
Bayeslite - BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
extending-jax - Extending JAX with custom C++ and CUDA code
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
objax