omnitool VS llama.cpp

Our you could use something like omnitool (https://github.com/omnitool-ai/omnitool) and interface with both cloud and local AI, not limited to llms.

Very interesting read. My name is Emmanuel Lusinchi, and together with my colleague Georg Zoeller, we represent Omnitool.ai, a fledgling platform in the AI landscape with a mission that resonates deeply with the ethos of Answer.AI.

Firstly, congratulations on the launch of Answer.AI and the vision you've set forth. We have been following your work with great admiration - I took the FastAI course in what feels like an eternity ago (3 years) and I have been recommending it to anyone interested in foundational AI. But more to your post’s point: your commitment to harnessing AI's potential to create practical end-user products is not only inspiring but aligns with our own philosophy.

We have developed an open-source "AI lab-in-a-box". A platform that seamlessly integrates a multitude of AI models, both locally and cloud-hosted, through a single unified interface. The aim is to simplify access to the latest developments in AI - both on the technical side (knowledge to run AI models and connect them together) and on the financial side (access to GPUs) . We believe this to be useful to accelerate experimentation and iteration but also facilitate teaching AI - giving teachers a simple, consistent tool and giving students practical experience with the latest models so they can experience first hand complex and often too abstract concepts such as Bias. By reducing friction and lowering barriers of entry, our platform aims at democratizing access to the latest AI technologies, providing almost anyone with the tools and flexibility needed to push the boundaries of what's possible with AI.

And we do believe that our platform could serve as a valuable tool in your R&D processes, speeding up Answer.AI ability to rapidly prototype and refine applications that leverage foundational research breakthroughs.

Moreover, we share your concern about the widening gap in understanding AI's capabilities and its implications. We believe that transparency, education, and open-source collaboration are key to bridging this gap, ensuring that AI's benefits are widely distributed and its risks are responsibly managed.

We are reaching out to explore potential avenues for collaboration. Whether it's simply helping you evaluate and perhaps use our platform into your R&D workflow, co-developing new tools, or simply engaging in a dialogue to share insights, we are eager to contribute to the incredible work you're undertaking at Answer.AI.

We would be honored to discuss this further with you. Please find more information about our platform and its capabilities on our GitHub: https://github.com/omnitool-ai/omnitool. We are also open to setting up a demonstration or a meeting at your convenience to explore synergies between our organizations.

Warm regards,

Emmanuel Lusinchi Co-founder, Omnitool.ai [email protected]

if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp

the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...

And you can search for any GGUF on huggingface

Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182

For anyone interested in exploring this, llama.cpp has an example implementation here:

https://github.com/ggerganov/llama.cpp/tree/master/examples/...

Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.

well https://github.com/ggerganov/llama.cpp/issues/6849