trax
ReplitLM
trax | ReplitLM | |
---|---|---|
7 | 3 | |
7,957 | 909 | |
0.4% | 0.4% | |
4.7 | 7.0 | |
3 months ago | 7 months ago | |
Python | Python | |
Apache License 2.0 | 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.
trax
-
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
-
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!
-
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.
-
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
-
[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?
-
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
ReplitLM
-
AI — weekly megathread!
Replit’s new 2.7B params code LLM, ReplitLM is now open-source. It outperformed Codex and LLaMA despite being smaller in size [GitHub | Hugging Face Demo].
-
Replit's new Code LLM was trained in 1 week
Some links:
- Repo: https://github.com/replit/ReplitLM/tree/main/replit-code-v1-...
- HuggingFace: https://huggingface.co/replit/replit-code-v1-3b
- Demo: https://huggingface.co/spaces/replit/replit-code-v1-3b-demo
- Early benchmark results: https://twitter.com/amasad/status/1651019556423598081
A lot about this was surprising. We knew it was going to be good, but didn't expect to be this good -- especially surprising was the finetuned performance boost and the fact that the model is decent (in some cases much better than much larger language models) at language tasks and reasoning.
It feels like there is a lot more to do with this model, and I have a suspicion you can even make a half-decent chat bot (at least one focused on code) by finetuning.
Will follow up with the UL2R version (fill-in-the-middle support).
- ReplitLM specialized on code completion open-sourced by Replit
What are some alternatives?
flax - Flax is a neural network library for JAX that is designed for flexibility.
code-align-evals-data
dm-haiku - JAX-based neural network library
aide - LLM shell and document interogator
muzero-general - MuZero
mation-spec
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
hate-speech-project
extending-jax - Extending JAX with custom C++ and CUDA code
thinkgpt - Agent techniques to augment your LLM and push it beyong its limits
objax
Mr.-Ranedeer-AI-Tutor - A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.