Auto-GPT
DeepSpeed
| Auto-GPT | DeepSpeed | |
|---|---|---|
| 104 | 51 | |
| 72,359 | 33,306 | |
| - | 1.7% | |
| 9.8 | 9.8 | |
| about 1 year ago | 6 days ago | |
| Python | Python | |
| MIT License | 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.
Auto-GPT
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How I am catching up with AI
A few notable examples of these leaps in AI technology include GPT-3.5, GPT-4, ChatGPT, Midjourney, Dall-e 2, AutoGPT, and Github Next.
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How to install Auto-GPT on Mac
Why did you clone git clone https://github.com/Torantulino/Auto-GPT.git instead of https://github.com/Significant-Gravitas/Auto-GPT.git Does it matter?
- [Termux] Comment exécuter Auto-GPT sur Android?
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Don’t Build Your House on Someone Else’s Land
You can also use the API key with tools such as TypingMind and Auto-GPT.
- [Chatgptpro] Auto-GPT (intento de código abierto para hacer que GPT4 sea completamente autónomo)
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[Machine Learning] [D] Que pensez-vous de ce problème sur Auto-GPT?
[https://github.com/torantulino/auto-gpt/issues/475
- [Chatgptpro] Auto-GPT (open source tente de rendre GPT4 entièrement autonome)
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How do I update Auto GPT ?
git clone --branch stable https://github.com/Torantulino/Auto-GPT.git $ git pull $ pip3 install -r requirements.txt
- [Singularity] Chaos GPT: Utilisation de l'auto-GPT pour créer un agent d'IA hostile mis sur la destruction de l'humanité
- FLiPN-FLaNK Stack Weekly for 17 April 2023
DeepSpeed
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
DeepSpeed can handle parallelism concerns, and even offload data/model to RAM, or even NVMe (!?) . I'm surprised I don't see this project used more.
- [P][D] A100 is much slower than expected at low batch size for text generation
- DeepSpeed-FastGen: High-Throughput for LLMs via MII and DeepSpeed-Inference
- DeepSpeed-FastGen: High-Throughput Text Generation for LLMs
- Why async gradient update doesn't get popular in LLM community?
- DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models (r/MachineLearning)
- [P] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
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A comprehensive guide to running Llama 2 locally
While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:
* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.
* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].
You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)
Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)
[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...
[2] https://github.com/microsoft/DeepSpeed/issues/1580
[3] https://github.com/TimDettmers/bitsandbytes/issues/485
[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...
[5] https://forums.macrumors.com/threads/ai-generated-art-stable...
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
And https://github.com/microsoft/deepspeed
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April 2023
DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales (https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)
What are some alternatives?
gpt4all - gpt4all: run open-source LLMs anywhere
ColossalAI - Making large AI models cheaper, faster and more accessible
llama.cpp - LLM inference in C/C++
Megatron-LM - Ongoing research training transformer models at scale
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
fairscale - PyTorch extensions for high performance and large scale training.
AgentGPT - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
babyagi
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.