[danielhanchen]

We made Unsloth Studio which should help :)

1. Auto best official parameters set for all models

2. Auto determines the largest quant that can fit on your PC / Mac etc

3. Auto determines max context length

4. Auto heals tool calls, provides python & bash + web search :)

[ryandrake]

Yea, I actually tried it out last time we had one of these threads. It's undeniably easy to use, but it is also very opinionated about things like the directory locations/layouts for various assets. I don't think I managed to get it to work with a simple flat directory full of pre-downloaded models on an NFS mount to my NAS. It also insists on re-downloading a 3GB model every time it is launches, even after I delete the model file. I probably have to just sit down and do some Googleing/searching in order to rein the software in and get it to work the way I want it to on my system.

[danielhanchen]

Oh my apologies I didn't respond - if only HN had a notifier haha

Oh yes we added a custom folder button which can pull .gguf files for now from any folder - it supports LM Studio and Ollama ones - but afreed it's still a mess.

One of the goals is to somehow quick search for .gguf folders, and add recommended folders - we currently have folders for Ollama and LM Studio for eg

[hypercube33]

Sadly doesn't support fine tuning on AMD yet which gave me a sad since I wanted to cut one of these down to be specific domain experts. Also running the studio is a bit of a nightmare when it calls diskpart during its install (why?)

[danielhanchen]

Apologies as well didn't reply sooner - Studio supports AMD out of the box now! We worked with AMD to make it work! One thing that is still missing is pre-compiled AMD ROCM binaries, which we're trying to see if we can integrate that.

Interesting on diskpart - let me check and get back to you [EDIT] - visual studio build tools, python 3.13, git, cmake, node.js are all msi-based installers - so these are likely the culprits on using diskpart - essentially MSI installers check if there's enough disk space before installing items

[Zopieux]

Thanks for that. Did you notice that the unsloth/unsloth docker image is 12GB? Does it embed CUDA libraries or some default models that justifies the heavy footprint?

[danielhanchen]

Hey so sorry didn't reply sooner - yes the docker used to be I think 4-8GB ish since CUDA sadly itself is 4GB I think, and PyTorch takes the rest. So unfortunately the Unsloth Docker image has ballooned due to this. We tried reducing it as much as possible, but it's hard :( https://hub.docker.com/r/vllm/vllm-openai/tags for eg is around 11GB ish, ad we're 13.6GB ish.

We'll try our best to compress it more, but it's tough

[WanderPanda]

I applaud that you recently started providing the KL divergence plots that really help understand how different quantizations compare. But how well does this correlate with closed loop performance? How difficult/expensive would it be to run the quantizations on e.g. some agentic coding benchmarks?

[danielhanchen]

Hey! Sorry for not replying sooner - yes we'll keep publishing more KLD - sadly some are saying we are "optimizing" for KLD now since we posted so many haha - but the whole purpose of quantization is to match the BF16 logits as much as possible whilst reducing disk space (ie reduce KLD).

In general so this is funny and a quirk of quantization - sometimes 8bit, 4bit models do BETTER on downstream benchmarks (SWE Bench for eg), since sometimes rounding can actually somehow act as a "regularization" method (this is just my hunch).

So KLD isn't that expensive, since we leverage the trick of causal attention - since causal attention is lower triangular, we can do 1 forward pass on the enter text (say 2048 tokens), and you attain logits for the prediction for every token's position - so this is O(N^2).

However coding benchmarking require actual inference, and cannot use the causal attention trick, and it's best to run them 10 times since temperature = 1.0 is not deterministic - and take an average. We plan to maybe do something like https://marginlab.ai/trackers/claude-code/, which takes a random sample and does it over time.

[cyanydeez]

Is unsloth working on managing remote servers, like how vscode integrates with a remote server via ssh?

[danielhanchen]

Hey sorry on the delay - we just added API support, so you can access a remote server - it includes optional python, tool call, bash and web search support if you enable them.

For SSH - we haven't yet done that - for now we have a SHA256 encryption approach, but it's not SSH yet. HTTPS will also sadly have to be the end user's setup process as well - we plan to make it better soon!

[kristjansson]

Lmstudio Link is GREAT for that right now

[danielhanchen]

Oh yes LM Link is cool!

[jbellis]

what are you using for web search?

[danielhanchen]

We use Duck Duck Go - sorry on the delayed response as well

[wuschel]

Great project! Thank you for that!

[danielhanchen]

Thank you and appreciate it! Sorry on the delayed reply as well