loopgpt VS DeepSpeed

So we thought it would be a good idea to create a framework that makes use of LoopGPT agent's memory and custom tooling capabilities. Let's jump right into the new features of this framework.

LoopGPT Modular Auto-GPT Framework (https://github.com/farizrahman4u/loopgpt)

We just added Azure OpenAI support on our modular reimplementation of Auto-GPT: LoopGPT. And I think it's much easier to setup using our python API because you can just use the copy-paste that you can get from "View code" on the Chat Playground on Azure. Here's the whole snippet that you need to use.

I suggest using LoopGPT, a GPT-3.5 friendly, modular re-implementation of Auto-GPT (this is self-promotion, I am a co-author FYI :). We have full state serialization which means you can save your agent state completely and start right from where you left off. To get started just do

As another MLE, who's working on an Auto-GPT inspired project, I want to say that I totally understand this technology (atleast with GPT-3.5) is not great. This is why we focused on building a better codebase, which is extensibile, modular and is just an overall more "Pythonic" reimplementation of Auto-GPT rather than trying to claim to be perfect. Although, I must mention that people have had better results with LoopGPT with both GPT-3.5 and GPT-4, and we don't even have GPT-4!

I suggest you try LoopGPT - It works better on GPT-3.5 according to many users. We have a nice little discord too where you can post any issues: https://discord.gg/rqs26cqx7v

Glad you got it figured out. Please also try out LoopGPT if you can - it works better with GPT-3.5

This 100%. Also, AutoGPT is not the best option when you want to add more capability, LoopGPT is as it has a framework to easily add more capabilities (called tools).

We recently released a GPT-3.5-friendly reimplementation of Auto-GPT, a "Pythonic" modular framework called LoopGPT that supports adding custom tools. Our first-time users tell us it produces better results compared to Auto-GPT on both GPT-3.5 as well as GPT-4.

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.

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...

And https://github.com/microsoft/deepspeed

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)