mlx_clip
Megatron-LM
| mlx_clip | Megatron-LM | |
|---|---|---|
| 1 | 19 | |
| 48 | 8,914 | |
| - | 4.0% | |
| 6.8 | 9.9 | |
| 2 months ago | 5 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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mlx_clip
Megatron-LM
- FLaNK AI Weekly for 29 April 2024
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Apple releases CoreNet, a library for training deep neural networks
https://github.com/NVIDIA/Megatron-LM
This is probably a good baseline to start thinking about LLM training at scale.
- Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
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Large Language Models: Compairing Gen2/Gen3 Models (GPT-3, GPT-J, MT5 and More)
This 20B model was trained on the same datasets as its predecessor, aptly named The Pile. Furthermore, the libraries Megatron and DeepSpeed were used to achieve better computing resource utilization, and eventually GPT-NeoX evolved into its own framework for training other LLMs. It was used, for example, as the foundation for Llemma, an open-source model specializing on theorem proving.
- Why async gradient update doesn't get popular in LLM community?
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[D] Distributes pre-training and fine-tuning
Deepspeed Megatron-LM
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Why Did Google Brain Exist?
GPU cluster scaling has come a long way. Just checkout the scaling plot here: https://github.com/NVIDIA/Megatron-LM
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Does Megatron-LM really not communicate during multi-head attention operations?
I found their code that the softmax function conduct all-reduce before they work.
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I asked ChatGPT to rate the intelligence level of current AI systems out there.
Google's PaLM, Facebook's LLaMA, Nvidia's Megatron, I am missing some surely and Apple sure has something cooking as well but these are the big ones, of course none of them are publicly available, but research papers are reputable. All of the ones mentioned should beat GPT-3 although GPT-3.5 (chatGPT) should be bit better and ability to search (Bing) should level the playing field even further, but Google's PaLM with search functionality should be clearly ahead. This is why people are excited about GPT-4, GPT-3 was way ahead of anyone else when it came out but others were able to catch up since, we'll see if GPT-4 will be another bing jump among LLMs.
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GPT-4 Will Be 500x Smaller Than People Think - Here Is Why
Found relevant code at https://github.com/nvidia/megatron-lm + all code implementations here
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
ColossalAI - Making large AI models cheaper, faster and more accessible
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
xla - Enabling PyTorch on XLA Devices (e.g. Google TPU)
ChatGPT-Siri - Shortcuts for Siri using ChatGPT API gpt-3.5-turbo & gpt-4 model, supports continuous conversations, configure the API key & save chat records. 由 ChatGPT API gpt-3.5-turbo & gpt-4 模型驱动的智能 Siri，支持连续对话，配置API key，配置系统prompt，保存聊天记录。