trax
hate-speech-project
trax | hate-speech-project | |
---|---|---|
7 | 1 | |
7,957 | 6 | |
0.4% | - | |
4.7 | 10.0 | |
3 months ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | - |
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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
hate-speech-project
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Replit's new Code LLM was trained in 1 week
My favorite line from the HumanEval paper
> It is important for these tasks to be hand-written, since our models are trained on a large fraction of GitHub, which already contains solutions to problems from a variety of sources.
So to answer your question, yes, the evaluation dataset is spoiled. You can find such unique and never before seen docstrings like
> For a given list of input numbers calculate the Mean Absolute Deviation around the mean of this dataset. Mean Absolute Deviation is the absolute difference between each element and a centerpoint (mean in this case)[0]
And here's a repo I found that is 8 years old[1]. But how about a more recent one that is even closer?[2] There's plenty more examples[3] (does anyone know how actually limit the date to prior to 2021? `pushed:<2021` doesn't work nor does using the `created` keyword. Date searching doesn't seem to work well).
[0] https://github.com/openai/code-align-evals-data/blob/97446d9...
[1] https://github.com/bertomartin/stat4701/blob/ec2b64f629cbbf6...
[2] https://github.com/danielwatson6/hate-speech-project/blob/64...
[3] https://github.com/search?q=abs%28x+-+mean%29+for+language%3...
What are some alternatives?
flax - Flax is a neural network library for JAX that is designed for flexibility.
IF
dm-haiku - JAX-based neural network library
ReplitLM - Inference code and configs for the ReplitLM model family
muzero-general - MuZero
code-align-evals-data
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
stat4701 - Final Project
extending-jax - Extending JAX with custom C++ and CUDA code
objax
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
jax-resnet - Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).