rllama VS alpaca.cpp

Location: San Francisco

Remote: No preference, as long as I don't have to move far from Bay Area

Willing to relocate: No

Technologies: C, Rust, Golang, Haskell, Lisp, Python, Lua, OpenGL, SQLite3, JavaScript, PostgreSQL, AWS EC2, S3, ECS, Batch.

Resume: https://www.linkedin.com/in/mikjuola

Email: [email protected]

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I've been working at the Bay Area since 2015, most recently at Pinterest. At work, I've done big data pipelines, designed some batch job systems, computing metrics, handling billing APIs, lots of Python, Go and Java and working with AWS, i.e. backend and data engineer stuff.

But I'm trying to look for work that's more in line with what I do on my free time: Challenging low-level C or Rust programming, machine learning implementations (see e.g. this thing I made https://github.com/Noeda/rllama/, graphics programming or research-type work, uncommon programming languages.

If you scroll through my random crap repositories you can see what kind of things I'm interested in: https://github.com/Noeda?tab=repositories

No, my project is called rllama. No relation to GGML. https://github.com/Noeda/rllama

I have seen some Rust attempts as well such as https://github.com/Noeda/rllama/ but they are still way behind the C++ ones. This seems like an interesting space to get into.

I ran it on a 128 RAM machine with a Ryzen 5950X. It's not fast, 4 seconds per token. But it's just about fits without swapping. https://github.com/Noeda/rllama/

I've counted three different Rust LLaMA implementations on r/rust subreddit this week:

https://github.com/Noeda/rllama/ (pure Rust+OpenCL)

https://github.com/setzer22/llama-rs/ (ggml based)

https://github.com/philpax/ggllama (also ggml based)

There's also a discussion on GitHub issue on setzer's repo to collaborate a bit on these separate efforts: https://github.com/setzer22/llama-rs/issues/4

Where's the 30B-in-6GB claim? ^FGB in your GH link finds [0] which is neither by jart nor by ggerganov but by another user who promptly gets told to look at [1] where Justine denies that claim.

  [0] https://github.com/antimatter15/alpaca.cpp/issues/182

You can just download the language model, dude!!! Everyone doesn’t need to make their own and the open source models literally get better every day.

Alpaca

Square could have also trained a more specific data source for their NLP, very similar to Alpaca. Alpaca was trained from interactions from a larger dataset. So while it isn't as smart, it's still able to understand instructions and act upon them.

The model I found was in 8 parts. The alpaca.cpp chat client (chat.cpp) needs to be modified to run the 8 part model, documented here: https://github.com/antimatter15/alpaca.cpp/issues/149

try the instructions on this github repo https://github.com/antimatter15/alpaca.cpp, its not the best one but I was able to run this model on my linux machine with 16GB memory, I think its a good starting point.

Doesn't run offline. If you're running something without a beefy-ish GPU, there's https://github.com/antimatter15/alpaca.cpp .

Ye, atm. toying with alpaca 7B/13B in a local install.