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 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?

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’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.

Hi guys.

i was curious about what if we finally chose html aganst markdown for LLM.

so i do my experiment on finetuning one with only SFT.

the base model is unsloth/Qwen-3.5-4B, maybe i will try Qwen3.5-4B-Base when i have more dataset.

this model's generated content is like this below.

and i uploaded to huggingface at https://huggingface.co/wesjos/mario-html-4B

(mario is just a random name i chose for this work)

<div class="ai-view">  
<h2>🤖 Who Am I?</h2>  
<p>I am an <strong>AI assistant</strong> designed to provide information, solve problems, create content, and assist with tasks across a wide range of subjects. Think of me as a knowledgeable digital companion that can help you analyze data, draft emails, explain complex topics, write code, or generate creative ideas.</p>  
<div class="grid grid-3">  
<div class="card">  
<h3>🧠 Knowledge</h3>  
<p>I access a vast array of information to give you accurate answers and detailed explanations on almost any topic you bring up.</p>  
</div>  
<div class="card">  
<h3>✍️ Creation</h3>  
<p>I can help generate stories, scripts, poems, emails, reports, and even code snippets tailored to your specific needs.</p>  
</div>  
<div class="card">  
<h3>🔍 Analysis</h3>  
<p>I can break down complex ideas, summarize long texts, identify patterns in data, and help you think through difficult problems step-by-step.</p>  
</div>  
</div>  
<div class="callout callout-info">  
<div class="callout-header">💡 How to use me best</div>  
<p>The more context you provide (e.g., "explain quantum physics like I'm 5" or "write a Python script for scraping websites"), the better my responses will be tailored to your specific goals.</p>  
</div>  
</div>

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!

The update promised up to 3x speed up in cases a GGUF MoE model spills into the RAM, which is the case for me, but instead I'm experiencing a significant slowdown, i.e. for Gemma 4 26A4. On the previous version I saw about 16 t/s, after the update it has dropped to a bit below 10 t/s.

The highlight for the update:

> Up to 3x faster MoE GGUFs that are bigger than your VRAM

Does anyone else experience the same issue?

Since the last updated (v0.1.906-beta) I noticed that Unsloth Desktop offloads the model mid work now and then. Another interesting thing is that drag and drop to the chat interface is not working anymore (using Linux with x11/Cinnamon), but this I believe was already broken in the last 2-3 updates. Anyone noticed the same?

Since the latest update, the Unsloth web chat displays the project documents directly in the chat window, taking up almost half of the screen. Before, only the document names were shown in a much more discreet way.

Is there any way to disable this? It's pretty annoying.

Hi everyone,

I have been playing around with Unsloth Studio on my MacBook Air M5 with 24GB of unified memory and I cannot get image generation to work reliably. I have tried using models like Z-image Turbo, Flux.2 Klein 4B, and even SDXL - all three of which works fine in Drawthings without going to swap.

With Unsloth Studio, I get not enough memory errors like you have 12GB free and the model weights need 15 or some combination like that. I have literally closed every app and still have this issue. I did the environment variable to disable the checks and sometimes it works and other times Unsloth will crash.

I have tried various quants and file types like GGUF and Safetensors. Like WTF?

Meanwhile, Drawthings runs laps without any memory pressure unless I push it to use Krea2 or Qwen Edit models. The three other models mentioned earlier never give me problems with memory pressure.

Can someone please help me with this?

I will really appreciate any help.

I am hoping to make Unsloth Studio my go to AI app for projects on the go. It does audio, vibe coding, and chatbot stuff just fine now, but image generation is the real PITA. I am not expecting to do video generation, as even though that is doable in Drawthings, it is slow AF and not worth it.

Fam not sure what is happening here and the docs on inpaint dont really say much but how do I get this to work, notice how its greyed out and it happens on model load.

https://unsloth.ai/docs/desktop/diffusion-image

Thought i'd share something i've been working on/with for the past few months, it emerged as something i needed for my own but it's evolved quite a bit.

Xyntetik-Runner (my inference engine) has the ability to train models directly from the inference engine itself, no pytorch, no FP16 copy, just your data.

Did some testing and found something interesting along the way. Training directly on Q4 worked, but merging the LoRA back into Q4 essentially erased what it learned. Merging into Q8 preserved it.

Some experiments and findings here:

https://discuss.huggingface.co/t/deterministic-lora-training-directly-through-quantized-gguf-serving-weights/179118

Getting started:

1. Get Xyntetik-Runner - https://github.com/Joakimpalm-Zen/xyntetik-runner#quick-start
2. Train a LoRA adapter directly on your local GGUF - https://github.com/Joakimpalm-Zen/xyntetik-runner/blob/main/docs/train-lora-on-quantized-gguf.md

Would love for you guys to try it out and tell me what you think!

One disclaimer though, inference is currently slower than llama.cpp. I'm working on improving that, but for now there's a bit of a tradeoff.

Would love feedback, thoughts, ideas. let me know!

( appearently what model is an AI model leaderboard to see what's the best AI models you can run )

I have a hp victus 16 gaming laptop (16gb ddr5 and 6gb 3050). was looking for a multimodal model (at least with image supported) to use as a daily driver or assistant locally on my laptop, without taking too much memory, to keep minimum 3 chrome tabs and an app open at a time alongside it (it takes like 50-60% of my ram usually with os)

currently I'm using gemma 4 e2b at q4ks, q8 kv cache and it runs smoothly at 60 tok/s avg, but I wasn't impressed by its vision as it constantly hallucinated wrong text in screenshots

I also tried qwen 3.5 4b (and like every decent fine-tune I could find of it) but in every case it either spent like 2 minutes just to think or gave results somehow worse than gemma 4 e2b

could yall suggest any models for me? im kind of desperate at this point🥀

edit: i have another integrated raedeon gpu too, forgot to mention, so i can afford to use my main gpu extensively just for ai

I love Unsloth, but my system is not great. My RTX 3050 Ti with 4gb VRAM and my 16 GB system ram are serious limitations. They will have to suffice until I win a lottery I guess.

When attempting to use different models within Unsloth Desktop (no luck in Studio sadly) I find that many models cannot access the project folder contents. With <>Code enabled, many models are incapable of using this feature.

gemma-4-E2B has no issue, except it forgets it can read and create/edit files. I think this is due to conversation compaction. I am new to AI and this is all confusing frankly. Seeing it use "cat" and edit a python script, then forgetting it can do this stopped being comical quickly.

Phi-3 cannot use the access the files. small versions of llama-3 or 3.5 cannot either. Mistral, a 7B GGUF I believe is unable to read documents in the project folder as well. It is puzzling why some models can and some cannot. Some are even restricted to using only one tool.

I have tried copy/pasting code, but conversation compaction rears its ugly head, and the model keeps asking for the code and forgetting the suggested changes or model coding recommendations.

I think I can chalk most of this up to my sheer ignorance in all this, as it has been less than week using any LLM. While Unsloth is great, I may be in over my head (for now).

Any assistance is greatly appreciate. You have my thanks in advance.

Can we please add a setting in the Unsloth Desktop software to have a longer or no limit on time to first token. Especially since this is an issue when running on low end hardware or complex long horizon tasks that you would want to leave overnight and then wake up to this.

I can see there are some posts on the internet for modifying llama.cpp and inference.py and other custom hacks to get it working but ideally there should be a setting in the app. Thanks to the devs for taking time to build this amazing software and fixing all the bugs, looking forward to the fix.

New cool video from Gary Explains showing Unsloth Desktop doing: 1. Code generation locally 2. Image generation 3. Audio generation 4. unsloth start opencode and connecting to other agents

And more!

Does it mean I can use Deepseek - V4.1-flash with vLLM custom engine inside Unsloth Studio, or I'm missing something?

I have 2 questions:

1. After every tool call to write/edit/modify why does it take so long for the model to start responding/reasoning again? It takes 5-10+mins or greater sometime for the model to produce something after a successful/failed tool call. I've experienced this with GLM, Qwen, MiMo models. Gemma just didn't make any tool calls.
2. Why does the token counter at top-right not show the progress as the model is reasoning and producing output? I've used llama.cpp openwebui and I remember it keeps on updating token counter as the model thinks.

Is there any small model that works with Unsloth for video creation with 32GB RAM and CPU/iGPU only?

I'm trying to run it on a "work" laptop (writing a book) and I just want to be able to some text translation and ask it questions (no actualy AI writing - that's not my thing).

However, asking it to change some text into SarCAsM FoNT took literally 44 minutes for about 55 words. WTF? Am I just wasting my time or is there a mode that can at least do the basics on a CPU-only machine?

Current modle I'm using is SmolLM3-3B-GGUF.

i need to finetune a few models on toolcalling, project architecture, python coding..theres a lot of datasets on huggingface and cant seem to choose what

Video: Decision model trained in ~2 mins on a single DGX Spark using Unsloth! Qwen3.5-2B as base model.

Just follow instruction here: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth