fred
trax
fred | trax | |
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4 | 7 | |
99 | 8,093 | |
- | 0.4% | |
5.9 | 5.8 | |
5 months ago | 2 months ago | |
Python | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
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fred
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Ask HN: Best way to learn robotics with a 10 year old?
Thanks, that's very interesting, bookmarked! I found https://github.com/thomashiemstra/fred whilst searching, which also uses dynamixel motors, but very expensive ones.
It does look very smooth motion-wise though!
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Anyone know of any good robotic arm programming simulators?
Another option: pybullet. If you just want to learn and you don't need a super high performant system go with python. As an example my code for controlling a simulated robot arm with pybullet my code
- Fred the Robot Arm – 6 DOF 3D printed dynamixel robot arm
- Obstacle avoidance using deep reinforcement learning on a 3d printed 6 DOF robot arm. Github in comments.
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?
BlossomNav - BlossomNav: A Low-Cost Software Suite for Mobile Socially Assistive Robots
flax - Flax is a neural network library for JAX that is designed for flexibility.