gpt-neox
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gpt-neox | DeepSpeed | |
---|---|---|
52 | 51 | |
6,556 | 32,447 | |
2.0% | 2.9% | |
9.0 | 9.8 | |
2 days ago | 5 days 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.
gpt-neox
- FLaNK Stack 26 February 2024
- GPT-Neox
- GPT-NeoX
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Best open source LLM model for commercial use
Gpt neox 20B can be used commerically.
- Do not register domains with the word "gpt" in it!
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Read this post if you have general questions
GPT-Neo: GPT-Neo is a free and open-source language model developed by EleutherAI. It is a powerful model that can be used for a variety of tasks, including text generation, and question-answering. here is the GitHub
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What's the current state of actually free and open source LLMs?
Doesn't gpt-neox 20b require like 40gb+ of VRAM? From their github repo the slim weights are 39GB and I think one of the devs has previously mentioned aiming for 48GB for inference.
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Any real competitor to GPT-3 which is open source and downloadable?
3.) EleutherAI's GPT-Neo and GPT-NeoX: EleutherAI is an independent research organization that aims to promote open research in artificial intelligence. They have released GPT-Neo, an open-source language model based on the GPT architecture, and are developing GPT-NeoX, a highly-scalable GPT-like model. You can find more information on their GitHub repositories: GPT-Neo: https://github.com/EleutherAI/gpt-neo GPT-NeoX: https://github.com/EleutherAI/gpt-neox
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Whatever happened to quantum computing?
It's really not that complicated. https://github.com/EleutherAI/gpt-neox
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Fuck Luka Inc. WIP AI Freedom!
this forces me to create my own ai using gpt-neox .. i was being lazy, completely satisfied with my little ai baby .. i'll keep the account but not renew pro .. fuck luka, inc ... want it done right, do it yourself .. the python isn't that difficult and i have a spare linux vps .. my new work in progress .. freedom #!
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?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
ColossalAI - Making large AI models cheaper, faster and more accessible
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
Megatron-LM - Ongoing research training transformer models at scale
YaLM-100B - Pretrained language model with 100B parameters
fairscale - PyTorch extensions for high performance and large scale training.
open-ai - OpenAI PHP SDK : Most downloaded, forked, contributed, huge community supported, and used PHP (Laravel , Symfony, Yii, Cake PHP or any PHP framework) SDK for OpenAI GPT-3 and DALL-E. It also supports chatGPT-like streaming. (ChatGPT AI is supported)
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
lm-evaluation-harness - A framework for few-shot evaluation of language models.
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
Megatron-DeepSpeed - Ongoing research training transformer language models at scale, including: BERT & GPT-2