maid VS llama.cpp

I've been trying this app but haven't had any luck getting it to actually generate text yet:

https://github.com/Mobile-Artificial-Intelligence/maid

The UI looks nice and includes a native compilation of llama.cpp.

My main phone's screen broke so I'm on an old Pixel 4 until it's repaired but I've had no luck getting 2-3GB models to run so far.

Try this: https://github.com/MaidFoundation/maid

This one uses cpu it makes less heat: https://github.com/MaidFoundation/maid

> Doing everything on-device would result in a horrible user experience. They might as well not participate in this generative AI rush at all if they hoped to keep it on-device.

On the contrary, I'm shocked over the last few months how "on device" on a Macbook Pro or Mac Studio competes plausibly with last year's early GPT-4, leveraging Llama 3 70b or Qwen2 72b.

There are surprisingly few things you "need" 128GB of so-called "unified RAM" for, but with M-series processors and the memory bandwidth, this is a use case that shines.

From this thread covering performance of llama.cpp on Apple Silicon M-series …

https://github.com/ggerganov/llama.cpp/discussions/4167

… "Buy as much memory as you can afford would be my bottom line!"

I'd love to use local models, but seems like most of the easy to use software out there (LM Studio, Backyard AI, koboldcpp) doesn't really play all that nicely with my Intel Arc GPU and it's painfully slow on my Ryzen 5 4500. Even my M1 MacBook isn't that fast at generating text with even 7B models.

I wonder if llama.cpp with SYCL could help, will have to try it out: https://github.com/ggerganov/llama.cpp/blob/master/README-sy...

But even if that worked, I'd still have the problem that IDEs and whatever else I have open already eats most of the 32 GB of RAM my desktop PC has. Whereas if I ran a small code model on the MacBook and connected to it through my PC, it'd still probably be too slow for autocomplete, when compared to GitHub Copilot and less accurate than ChatGPT or Phind for most stuff.

You're correct if you're focused exclusively on the work surrounding building foundation models to begin with. But if you take a broader view, having open models that we can legally fine tune and hack with locally has created a large and ever-growing community of builders and innovators that could not exist without these open models. Just take a look at projects like InvokeAI [0] in the image space or especially llama.cpp [1] in the text generation space. These projects are large, have lots of contributors, move very fast, and drive a lot of innovation and collaboration in applying AI to various domains in a way that simply wouldn't be possible without the open models.

[0] https://github.com/invoke-ai/InvokeAI

[1] https://github.com/ggerganov/llama.cpp

The first example will build an Embeddings database backed by llama.cpp vectorization.

and at some point (https://github.com/ggerganov/llama.cpp/issues/7444)

git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp LLAMA_CUDA=1 make -j

I focused on supporting Ollama, GPT4All, and LMStudio, all of which run smoothly on a Mac computer. Many of these tools are user-friendly wrappers around Llama.cpp, allowing easy model downloads and providing a REST interface to query the available models. Last week, I also added "👋🏼 Jan" support because HuggingFace has endorsed this provider out-of-the-box.

The output of the LLM is not just one token, but a statistical distribution across all possible output tokens. The tool you use to generate output will sample from this distribution with various techniques, and you can put constraints on it like not being too repetitive. Some of them support getting very specific about the allowed output format, e.g. https://github.com/ggerganov/llama.cpp/blob/master/grammars/... So even if the LLM says that an invalid token is the most likely next token, the tool will never select it for output. It will only sample from valid tokens.