Uv helps you up though. Use a pyproject.toml and uv sync. Everything will be put into the venv only, nothing spread across the whole system.
The pyproject.toml can even handles build env for you, so you no longer need a setup.sh that installs 10 tool in specific order with specific flag to produce working environment. A single uv sync, and the job is done.
Plus the result is reproducible, so if this time uv sync work, then it also work next time.
Highly recommend if you are still on pip.
Note: Take a example that I used to install unsloth with rocm setup that based on unreleased git version dependencies and graphic card specific build flag, all of them can be handled with one command 'uv sync'. This will require a big pile of shell script if doing another way. https://github.com/unslothai/unsloth/issues/4280#issuecommen...
Ah yes, came to say something similar, Python dependencies are an absolute nightmare, even with uv it feels like there's always a battle to make other peoples Python apps install.
Update: It looks like it doesn't work with the current Python version, you might have to downgrade to Python 3.13 (however even then I still get `error: unexpected argument '--torch-backend' found`)
Hey! Our primary objective for now is to provide the open source community with cool and useful tooling - we found closed source to be much more popular because of better tooling!
Thank you for the follow up! Big fan of your models here, thanks for everything you are doing!
Works fine on MacOS now (chat only).
On Ubuntu 24.04 with two GPU's (3090+3070), it appears that Llama.cpp sometimes uses the CPU and not GPU. This is judging from the tk/s and CPU load for identical models run with US-studio vs. just Llama.cpp (bleeding edge).
LM Studio user here. Unsloth looks great, wanted to check it out, but there is no app file to download and install? Sorry I'm not familiar with the command line.
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[ 4.9 ms ] story [ 72.4 ms ] threadIs it like, for AI hobbyists? I.e. I have a 4090 at home and want to fine-tune models?
Is it a competitor to LMStudio?
This needs to go on homebrew or be a zip file with an app for manual download.
The pyproject.toml can even handles build env for you, so you no longer need a setup.sh that installs 10 tool in specific order with specific flag to produce working environment. A single uv sync, and the job is done.
Plus the result is reproducible, so if this time uv sync work, then it also work next time.
Highly recommend if you are still on pip.
Note: Take a example that I used to install unsloth with rocm setup that based on unreleased git version dependencies and graphic card specific build flag, all of them can be handled with one command 'uv sync'. This will require a big pile of shell script if doing another way. https://github.com/unslothai/unsloth/issues/4280#issuecommen...
Update: It looks like it doesn't work with the current Python version, you might have to downgrade to Python 3.13 (however even then I still get `error: unexpected argument '--torch-backend' found`)
We have much much in the pipeline!!
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv unsloth_studio --python 3.13
source unsloth_studio/bin/activate
uv pip install unsloth --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888
Works fine on MacOS now (chat only).
On Ubuntu 24.04 with two GPU's (3090+3070), it appears that Llama.cpp sometimes uses the CPU and not GPU. This is judging from the tk/s and CPU load for identical models run with US-studio vs. just Llama.cpp (bleeding edge).
Is there an alternative, tutorial, or project you'd recommend that would help me do supervised fine tuning (SFT) with the metal stack / macOS?
However, since I already have pi working with llama.cpp server from a docker container, I did a quick experiment to compare three code bases:
https://gist.github.com/ontouchstart/7483c12efa3c3d3a49e38c2...
https://gist.github.com/ontouchstart/217fe2b8103a5c0bfaee1e9...
Very interesting.
Will do it again next week if I can get unsloth studio working.