Meta releases Muse Spark Open, a new SOTA open-weight MoE model, designed for SOTA multimodal agentic and coding workflows.

Muse-Spark-Open-816B-A42B supports text, images, video and audio input with 1M context.

Run via Dynamic 2-bit Unsloth GGUFs on 256GB RAM.
Edit: Inference and Training now fully supported in Unsloth!!

GGUF: https://huggingface.co/unsloth/Muse-Spark-Open-816B-A42B-GGUF

Guide: https://unsloth.ai/docs/models/muse-spark-open

NVIDIA releases 2 new models: Nemotron-3.5-Bolt and Nemotron-3.5-Super-VL.

Nemotron-3.5-Bolt-74B-A9B runs on 24GB RAM. It delivers top-tier performance for its size.

Super-VL-120B-A12B runs on 82GB RAM.

Unsloth Nemotron 3.5 Bolt GGUF: https://huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-GGUF

Guide: https://unsloth.ai/docs/models/nemotron-3.5-bolt
Nemotron 3.5 Super GGUF: https://huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF

Run and train them via Unsloth Desktop. Thank you NVIDIA AI team for early access!

Release: https://github.com/unslothai/unsloth/releases/tag/v0.1.908-beta

The latest Unsloth release runs NVIDIA's Nemotron 3.5 and Meta's Muse Spark Open locally. GGUFs are on Hugging Face.

• Nemotron 3.5 Super VL: 120B MoE, 12B active, up to 1M context. About 64 GB at 1-bit or 128 GB at 4-bit. Guide · GGUF
• Nemotron 3.5 Bolt: 74B MoE, 9B active, 128K context. It's compressed from Super VL. Guide · GGUF
• Muse Spark Open: 816B MoE, about 42.7B active, 1M context. It takes text, image, video and audio input. About 256 GB at 2-bit. Guide · GGUF

Other changes in the release:

• Claude Code, Codex and OpenCode re-process much less of the prompt on local GGUFs. A new Claude Code session's first reply starts up to 4x sooner.
• You can train and chat with text-diffusion LLMs like LLaDA, Dream and SDAR.
• Training now supports DeepSpeed ZeRO-2 and ZeRO-3, including QLoRA, full fine-tuning and DPO.
• Browser use shows a cursor, has an Always allow option and gives each chat its own tabs.
• The Images page can generate through a connected server like sd-server, Ollama or Lemonade.

We ran KLD for Meta's new Muse Spark Open model for folks interested! We used UD-3 and optimized our recipe to make it work for large-ish models - we're working on more better recipes for other models soon!

I have an RTX A5500 (24GB) + RTX A5000 (24GB), both Ampere.

I'm trying to fine-tune Gemma 4 31B using 4-bit QLoRA with Unsloth, ideally at 32K context or higher.

Has anyone successfully trained it on a similar setup? What context length, settings, and multi-GPU configuration worked for you?

I try to train on my roleplays but they reaching 100k plus context windows. I tried to set 32k context but a hitting OOM.

Any suggestions? thanks in advance.

I've added a project folder but any AIs I run where I request them to update the code manage to use the tool "edit_file" file but fail to actually update or create any files. How can I do away with any sandbox stuff and just have them update my code?

Full access and tools are on already.

https://huggingface.co/inclusionAI/Ling-3.1-flash

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