TaskMatrix VS DeepSpeed

I want to create a script that would automatically read telegram posts and process them, for this, using APIs these days is simply not reliable, so I'm thinking of using selenium python with the browser to do this..is there a way to develop an automated workflow that does this? I've looked in visual GPT from Microsoft https://github.com/microsoft/TaskMatrix

Low-code LLM (https://github.com/microsoft/TaskMatrix/tree/main/LowCodeLLM)

Yep, this is what Microsoft and others have done with the API to create things like bing chat. See: https://github.com/microsoft/TaskMatrix

Visual ChatGPT (now renamed as TaskMatrix https://github.com/microsoft/TaskMatrix likely as a result of OpenAI trying to regulate the use of the name GPT. Same happened for GPT-Eval -> G-Eval).

Microsoft is also going to connect millions of APIs. Right now software for the most part lives in silos and needs humans to move information from one software application to another. TaskMatrixAI will automate that by having a standard API schema that everyone buys into like Apple’s iOS if they want to make money. GPT and Jarvis automatically write the Python code to connect any desired APIs. https://github.com/microsoft/TaskMatrix/tree/main/TaskMatrix.AI

microsoft/TaskMatrix (github.com)

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)