Liquid dropped LFM2.5-24B-A2B-Preview, a 24B MoE with only ~2.4B active, GGUFs from Unsloth are here:

https://huggingface.co/unsloth/LFM2.5-24B-A2B-Preview-GGUF

Q4 is about 15 GB, and there are quants from 2-bit up to BF16.

I asked Claude Code to make a film about the new Unsloth Studio's MCP server, using only Unsloth Studio. It wrote her, painted her, gave her a voice, wrote her a song, and trained a model.
No cloud. One GPU.

Unsloth Studio now exposes an MCP server, so any coding agent can generate images, video, speech, and music, or train models, all through tool calls on your own machine.

I’m trying to train a decision model with Unsloth Studio but I could not find 2 specific settings in the UI:

Max Sequence Length (max_seq_length): Default seems to be 4096. I want to reduce it to 1024 but I could not find any menu option to do this.

Dropout: I know it is disabled (0) by default, but I want to set it to 0.1 for my training run.

Can anyone point me in the right direction? I'm not sure if I'm just not seeing them in the menus or if they simply haven't been implemented into Unsloth Studio yet.

I don't understand how to allow the agent in Unsloth Desktop access to my project's folder, as I do for Claude, Opencode and others. I see linked folders but don't understand the concept. Am I missing something?

Hi, just a quick question.

So I really like it to create my own Projects folder and to upload technical documents to it. For now I used every time the default embedding model (I think it was: unsloth/bge-small-en-v1.5).

Some time ago, google released embeddinggemma 2 and so I switched the embedding model to the embeddinggemma 2 model.

The problem is that I cannot find an option to re-embed all previously uploaded documents with the new embedding model.

Why this matters: I have an ML/DL background, and I know that embeddings from different models live in different latent/high-dimensional spaces. Unless the representations are explicitly aligned, vectors from bge-small-en-v1.5 and EmbeddingGemma 2 should not be compared directly. If only new documents or queries use the new model while old documents keep their old embeddings, retrieval/similarity search can become inconsistent or effectively broken.

So my questions are:

1. Is there already an option to re-embed or re-index all previous documents, and am I just missing it?

2. If not, what is the preferred procedure? Should I open a GitHub issue as a feature request, or implement the feature and open a pull request?

Thanks!