LLaMA-Factory VS pgmq

Depends what model you want to train, and how well you want your computer to keep working while you're doing it.

If you're interested in large language models there's a table of vram requirements for fine-tuning at [1] which says you could do the most basic type of fine-tuning on a 7B parameter model with 8GB VRAM.

You'll find that training takes quite a long time, and as a lot of the GPU power is going on training, your computer's responsiveness will suffer - even basic things like scrolling in your web browser or changing tabs uses the GPU, after all.

Spend a bit more and you'll probably have a better time.

[1] https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#...

And lots of interesting extensions use it, like

https://github.com/tembo-io/pgmq

https://github.com/zombodb/zombodb

https://github.com/supabase/pg_jsonschema

Have you considered https://github.com/tembo-io/pgmq for the queue bit?

some notes about pgmq, https://github.com/tembo-io/pgmq, that is on this list. It is built as an extension in Postgres, which makes it compatible with all languages that have a Postgres driver.

There's no 'magic' to it, it uses existing Postgres features so all the performance and consistency guarantees of Postgres are to be expected. Easily gets to 10k+ concurrent reads and writes even on smaller sized Postgres instances, which is more than most applications need.

Newer project but there's no library needed. https://github.com/tembo-io/pgmq. They have a pretty simple SQL api similar to SQS. It's an extension though, so some cloud provider will not support it.

PGMQ does not require a client library, https://github.com/tembo-io/pgmq so long as your language of choice can run SQL. All the functions live in Postgres, and you just call them with SQL statement. Very similar feel and semantics to SQS.