u/yoracale

Google releases EmbeddingGemma 2, a new open model that runs locally on 0.5GB RAM.

It supports text, code, images, video and audio, with 740M parameters: text (270M), vision (170M), audio (300M).

Run & train the model via Unsloth Desktop. We're going to make the text model the default embedding model now most likely.

GGUF: https://huggingface.co/unsloth/embeddinggemma-2-GGUF

Guide: https://unsloth.ai/docs/models/embeddinggemma-2

Hey guys rather than just using Hugging Face, you can now connect ModelScope to Unsloth. This is especially useful for folks in countries were Hugging Face is blocked!

There will also be a new release today with many new features! https://github.com/unslothai/unsloth

Our ModelScope: https://modelscope.cn/models/unsloth/

Sorry guys for the bugs/issues. Please update to the latest version. Now also with image editing. GitHub: https://github.com/unslothai/unsloth

Remember you can run both GGUF and FP8

Hey guys, Qwen-Image-2.1 can now run locally on 12GB VRAM with Unsloth GGUFs!

The 7B parameter model performs on par with Nano Banana 2.0.

Run our Dynamic FP8 or GGUFs for higher quality via diffusers, Unsloth Desktop & more. For higher quality, you can also run Dynamic FP8 on just 6GB of VRAM via offloading (<2x slower).

GGUF: https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF

Guide: https://unsloth.ai/docs/models/qwen-image-2.1

FP8: https://huggingface.co/unsloth/Qwen-Image-2.1-FP8

Please update to the latest Unsloth release. We also added Agent skills, performance improvements, a UI/UX overhaul and more! Thank you! :)

Edit: Should now be fixed - please update to the latest .14 version. Image gen and editing are supported

DeepSeek’s new 552B-parameter MoE model has a new asymmetric architecture with 196B engram parameters (making it slightly more accessible). Its new Causal Encoder–Decoder uses just 8B active parameters for input and 16B for output, while new pre-training and larger-scale RL help push benchmark performance beyond models like DeepSeek-V4-Pro.

Model link: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash

The model hit 10M downloads and 3.7K likes in just 24 days on Hugging Face - all thanks to you. 🤗🦥

GGUF: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF

Guide: https://unsloth.ai/docs/models/qwen3.8

Once again thanks for all your support!

Hey guys, we made local GLM-5.3-Flash GGUF inference now 1.6–3.4× faster with optimized decoding and bonus MTP support (bonuses dont just come from MTP). This is compared to our day-zero/one implementation so expect a lot of improved usability.

Our improvements are especially effective for long contexts.

Run 3-bit on 128GB setups via Unsloth Desktop or llama.cpp.

Guide: https://unsloth.ai/docs/models/glm-5.3-flash#faster-inference-and-mtp-support

GGUF: https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF

Everything works out of the box in Unsloth Desktop, simply update to the latest version if needed. No additional modules or MTP files are required.

Thank you!

Works via Hermes Desktop.

If you want to use Unsloth API with tool-calls, tool-calling healing, websearch etc enabled, you can still use Unsloth start: https://unsloth.ai/docs/integrations/hermes-agent

Hey guys, Qwen3.8-Flash-Next can now run 1.3-1.7× faster locally with no accuracy change via MTP! ⚡️ We released a new Unsloth Desktop release with MTP should be enabled by default and you can change the draft tokens. We also made the llama.cpp PR for it which you can read instructions for in our guide.

Qwen3.8-Flash GGUFs can reach 170 tokens/s on a RTX PRO 6000 which is insane!!

GGUFs: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF

Guide: https://unsloth.ai/docs/models/qwen3.8-next#mtp-guide

Thanks so much guys!

Hey guys, can now run GLM-5.3 with our GGUF! 🔥 GLM-5.3 is the strongest open model to date.

Since GLM-5.3 keeps the same size and architecture as GLM-5.2, most local hardware requirements and settings remain the same. Thanks Z ai for giving Unsloth day-zero access.

The 2-bit model retains ~81% accuracy after we shrunk it from 1.51TB to 239GB (-83% size).

Run the 2-bit on a 256GB Mac or RAM/VRAM setups.The other quants are still uploading

Guide: https://unsloth.ai/docs/models/glm-5.3

GGUF: https://huggingface.co/unsloth/GLM-5.3-GGUF

Thank you!

Hey guys thanks for the support this week! ♥️🦥

All Qwen3.8-Flash quants are now up including 4-bit to BF16.

The model is also supported in Unsloth as you already know!

Download them here: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF

GLM-5.3-Flash, also known as ox-alpha, is Z.ai’s new 320B parameter multimodal open model with 18B active parameters. GLM-5.3-Flash outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude 4.8 Opus on coding and agentic benchmarks.

Model: https://huggingface.co/zai-org/GLM-5.3-Flash

Docs we're working on: https://unsloth.ai/docs/models/glm-5.3

GGUF coming 'soon' at: https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF

We hope to update yall more rapidly today/tomorrow

We are working on supporting the model!

Model link: https://huggingface.co/Qwen/Qwen3.8-Flash-Next
GGUF link: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF

Guide: https://unsloth.ai/docs/models/qwen3.8-next

Hey guys, you can now fine-tune Qwen3.8-27B for free with our notebook! 🔥

Local training works on 24GB VRAM. Unsloth trains Qwen3.8 1.5x faster with 50% less VRAM than other setups with FA2.

We utilize many kernels including our own and others like Flash Linear Attention kernels for maximum performance.

And yes the notebooks are completely free thanks to Kaggle's 2x GPUs for use.

GitHub: https://github.com/unslothai/unsloth

Qwen3.8-27B Notebooks + Guide: https://unsloth.ai/docs/models/qwen3.8/train

Thank you! :)

Hey guys, Qwen announced Qwen3.8-Flash-Next, a new open-weight multimodal MoE model. 💜 The model will be released tomorrow and we are working on 'hopefully' Unsloth day zero support (the architecture is very new so it might take much longer but we are hopefully working towards it).

Qwen3.8-Flash-Next is built on the next-generation of Qwen4 architecture.

Also we did a new Unsloth desktop release today including auto compaction, keyboard shortcuts, LAN remote access amongst many many other features.

We’re releasing new Qwen3.8-27B GGUFs with 10% higher accuracy.

Unsloth Dynamic V3 outperforms others by >10% on Div-300, KLD & more benchmarks.

We also release 1-bit quants that retain 77% accuracy. Run on 8GB RAM.

Blog: https://unsloth.ai/docs/basics/dynamic-3.0-ggufs

GGUF: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF

Thanks to you guys, Qwen3.8-27B Unsloth GGUF is now the #2 trending model on Hugging Face with 2.7M downloads! 💗

Unsloth also reached #3 trending on GitHub!

Thanks so much for the love! Announcement coming tomorrow morning :)

Model: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF

GitHub: https://github.com/unslothai/unsloth