github unslothai/unsloth February-2026
12x Faster MoE Training + Embedding support!
on GitHub

15 hours ago

Our first release of 2026! This year we’ve got a lot of exciting things coming and to kick things off, we’re introducing faster MoE training, embedding model support, and ultra long context for Reinforcement Learning. We’ll also be launching our brand new UI very soon.

We’d like to thank all of you for 50K stars on GitHub! ⭐

february release

We’ve also added support for many new models that you can now run and fine-tune locally, including DeepSeek-OCR 2, GLM-4.7-Flash, Kimi-2.5, and more.

🚀 Faster MoE training

You can now train MoE models 12× faster with 35% less VRAM and 6x longer context via our new Triton and math kernels (no accuracy loss). gpt-oss-20b works on 12.8GB VRAM. Qwen3-30B-A3B (16-bit LoRA) uses 63GB.

Unsloth supports fast training for gpt-oss, Qwen3 (30B, 235B, VL, Coder), DeepSeek R1/V3 arch and GLM (4.7, Flash) models.

Faster MoE Blog

🔎 Embedding models now train 2× faster

We collaborated with Hugging Face to enable 1.8-3.3x faster embedding, BERT and classifier model training with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups.

Embedding model Blog

💡 Ultra Long Context RL is here

We’re introducing new batching algorithms to enable ~7x longer context (can be more than 12x) RL training with no accuracy or speed degradation vs. other optimized setups that use FA3, kernels & chunked losses.

Unsloth now trains gpt-oss QLoRA with 380K context on a single 192GB NVIDIA B200 GPU

Long Context RL Blog

🔮 New models

🎉 Extra Updates

  1. As part of our MoE release, we also made Gemma-3 now use Flex-Attention by default, and this works in float16 settings as well (there were infinities which we solved a while back). Gemma-3 now uses O(N) memory and not O(N^2) memory, and trains >3x faster (scales even better with context length). Previous Unsloth versions would OOM.
  2. Vision fine-tuning now accepts mixed data of only images and text data!
  3. trl==0.27.1 and transformers==5.1.0 are supported well - previous coverage was 30% of all our 120 notebooks, but now we have >80% coverage - we plan to make it 100% over the next few days.
  4. And many many other bug fixes and other updates!

📖 New Guides

  • </> How To Use Claude Code + Codex with local LLMs: Guide
  • 👾 Train & deploy to LM Studio for local inference: Guide
  • 🎨 Run Diffusion image models with Unsloth GGUFs: Guide

Tip

Update Unsloth via pip install --upgrade --force-reinstall --no-cache-dir --no-deps unsloth unsloth_zoo
If you want PyTorch 2.9: pip install --upgrade unsloth unsloth_zoo

February is shaping up to be an amazing month for LLM releases, and we hope you’re just as excited as we are. 😊

What's Changed

Unsloth Zoo Changes

Unsloth Notebooks changes

New Contributors

Full Changelog: December-2025...February-2026

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