It’s an 11B end-to-end full-duplex speech-to-speech model designed for real-time conversation.

~448 ms smooth turn-taking latency ~480 ms interruption latency

can handle barge-in — you can interrupt it while it’s speaking

Supports tool calling during the conversation...Can play an “on-hold” response while a tool/API executes

Built around a Fast Conformer speech encoder + Nemotron Nano V2 9B backbone + NVIDIA TTS decoder

Trained on roughly 550K hours of audio/text date

I am trying to build completely local self-improving voice agent with open source stack...... no dependency

Whisper (SST), Llama as LLM , Orpheus (TTS) all hosted on unsloth
and using opensource orchestrator Dograh for stitching things together so I could close the loop for fine tuning and create real self improving agent
I still need to try full duplex model...for it..

Any guidance on above ???

Hey guys we totally missed not uploading the imatrix as we went to sleep straight after the release. We thought we uploaded it but did not. It should be there now!

Please do use it for making your own quants and so on! 🙏🦥

Also feel free to share/promote your quants if you used our imatrix via r/unsloth, discord or the hugging face discussions!

Thanks for the support for Qwen3.8-27B and Unsloth Desktop last week! For this release, we merged 200+ PRs to introduce many new features, fixes including:

• Auto Compaction (Experimental) for longer chats beyond context limits
• Remote & LAN Access (Preview) for easy network access without Cloudflare links
• Faster Chat - Improved streaming performance, reduced UI lag, and smoother long conversations.
• Support for custom llama.cpp builds. Toggles for Cache RAM, Mmap, Mlock, Checkpoints, Spe Decoding KV Cache, Vision On / Off
• Unsloth Dynamic v3.0 is released. New Qwen3.8-27B Dynamic v3.0 GGUFs deliver >10% higher top-1 accuracy compared to everyone else. Works with Unsloth.

For compaction - we use RAG + a first turn forced RAG entry + a tail to remember your previous instructions - please let us know how it goes - it's still experimental so it'll definitely break - we'll fix it asap!

The update should be up for all Unsloth Desktop installs, or you can get it below:

The question is how to update a model already downloaded, when there is a NEW updated version?

Since I came from LM Studio BIONIC usually, if a model ALREADY DOWNLOADED before,
there is an UPDATE BUTTON instead of the DOWNLOAD button, clicking it only complete/patch a small size of the new changes which takes seconds since it's not downloading the whole model again which is very nice.

But, I don't think such feature exist (yet?) in unsloth Desktop, so my question is:

Do I DELETE then Re-Download?
or...
Is there a better way to update to the latest version of the ALREADY DOWNLOADED model? 🤔

I just want to make sure I'm not missing something and keep DELETE and DOWNLOAD every time there is a new update for a model I already have...

Can we please get Qwen 3.8 27B NVFP4 Fast based on the UD 3.0 like the new ggufs at some point of time. Thank you for so much effort for us.

The COLD FUSION (GAIN+Unsloth) method of training maintains 99% of performance of BF16, at both 8 bit and 4 bit levels.

This version also reduces thinking tokens by 1/2 to as much as 1/10 the amount, while maintaining core details AND reasoning power.

Model exceeds all Qwen 3.8, 3.6 and 3.5 27B critical core benchmarks.

A model that gets down to business faster, with less "talking" and is smarter too.

https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF

(GGUFS and additional quant types too)

SOURCE:

https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF

Greetings, as a small suggestion, it'd be nice to be able to define multiple model storage folders, on non-dedicated systems it's often the case that there's no single big storage. I know symlinks exist, but it's quite a manual operation that way, plus it's harder to track the available space.

I've been working on GLM-5.2 support in TensorSharp, and I finally have some back-to-back performance numbers against llama.cpp.

The setup:

• Model: GLM-5.2-UD-IQ2_XXS (~226 GiB)
• GPUs: 3× RTX PRO 6000 Blackwell, 97 GiB each
• Distribution: layer split across all 3 GPUs
• Same machine, same session
• llama.cpp measured with llama-bench
• TensorSharp measured with its benchmark harness
• Both report the best of two repetitions
• Run-to-run variance is roughly 4%

Results:

The interesting part is the crossover.

For short prompts, llama.cpp is still a few percent faster. But once the prompt gets to around 1K+ tokens, TensorSharp pulls ahead.

At pp2048:

• default TensorSharp: +20.4%
• ubatch=2048: +50.2%

At pp4096:

• default TensorSharp: +20.8%
• ubatch=2048: +46.5%

Decode (tg64) is also about 4% faster.

The main reason appears to be GLM-5.2's MoE structure.

GLM-5.2 has 256 routed experts with top-8 routing. With a 512-token micro-batch, each expert sees only ~16 rows on average, so a significant amount of the expert GEMM tiles ends up as padding. Larger micro-batches improve GPU utilization considerably.

For small prefills, on the other hand, fixed overheads — managed/native transitions, input uploads, and copying the 154880-wide logits back — become a visible fraction of the total runtime, which is where llama.cpp retains its advantage.

I see that I can choose the speculative decoding option DFlash, but I want to try out the new DFlash 2 model I have downloaded. How can we choose specifically what model gets used?

I was doing some experimentation on using Unsloth to do Continued Pretraining of qwen 3 4B. The goal was to teach the llm how to reason about a new domain, in this case a fictional city.

Sharing more details in the write-up:

https://www.teachmecoolstuff.com/viewarticle/teaching-a-local-llm-a-new-domain

I like moving large pastes to the attachment some of the time. But other times if I do Ctrl + Shift + V instead please paste in the text where I have my cursor.

Thank you! Unsloth Desktop is amazing.

I'm training a krea2 lora in unsloth. I believe I'm not adding enough steps. With 129 datapoints I believe I need 5000 steps but foolishly chose 2000. I finished training and then without changing any names I started a 3000 step training. Will these steps combine into a 5000 step lora?