You can now train LLMs 3× faster with no accuracy loss, via our new RoPE and MLP kernels. Our Triton kernels plus smart auto packing delivers ~3× faster training & 30% less VRAM vs optimized FA3 setups. Train Qwen3-4B 3x faster on just 3.9GB VRAM. Blog: docs.unsloth.ai/new/3x-faste...

You can now train OpenAI gpt-oss with Reinforcement Learning in our free notebook! This notebook automatically creates faster kernels via RL. Unsloth RL achieves the fastest inference & lowest VRAM vs. any setup - 0 accuracy loss gpt-oss-20b GRPO Colab: colab.research.google.com/github/unslo...

We're teaming up with Mistral and NVIDIA for an Unsloth event on Tues, Oct 21 at Y Combinator's office! 🦥 Join us in San Francisco for a night of talks, merch and more. Food & drinks provided. RSVP required! ⭐ lu.ma/unsloth-yc

Run DeepSeek-V3.1 locally on 170GB RAM with our Dynamic 1-bit GGUFs!🐋 The 715GB model gets reduced to 170GB (-80% size) by smartly quantizing layers. The 1-bit GGUF passes all our code tests & we fixed the chat template. Guide: docs.unsloth.ai/basics/deeps... GGUF: huggingface.co/unsloth/Deep...

Learn to fine-tune OpenAI gpt-oss with our new step-by-step guide! ✨ Learn about: • Local gpt-oss training + inference FAQ & tips • Evaluation, hyperparameters & overfitting • Reasoning effort, Data prep • Run & saving your model to llama.cpp GGUF, HF etc. 🔗Guide: docs.unsloth.ai/basics/gpt-o...

We made a complete Guide on Reinforcement Learning (RL) for LLMs! Learn about: • RL's goal & why it's key to building intelligent AI agents • Why o3, Claude 4 & R1 use RL • GRPO, RLHF, DPO, reward functions • Training your own local R1 model with Unsloth 🔗 docs.unsloth.ai/basics/reinf...

You can now run DeepSeek-R1-0528 locally with our Dynamic 1-bit GGUFs! 🐋 We shrank the full 715GB model to just 168GB (-80% size). We achieve optimal accuracy by selectively quantizing layers. DeepSeek-R1-0528-Qwen3-8B is also supported. GGUFs: huggingface.co/unsloth/Deep...

Introducing Unsloth Dynamic v2.0 GGUFs! v2.0 sets new benchmarks on 5-shot MMLU + KL Divergence. So, you can now run quantized LLMs with minimal accuracy loss. For benchmarks, we built an evaluation framework to match official MMLU scores of Llama 4 & Gemma 3 Blog: docs.unsloth.ai/basics/dynam...

You can now run Llama 4 on your local device! 🦙 We shrank Maverick (402B) from 400GB to 122GB (-70%). Our Dynamic 1.78-bit iMatrix GGUFs ensures optimal accuracy & size by selectively quantizing layers. Scout + Maverick GGUFs: huggingface.co/collections/... Guide: docs.unsloth.ai/basics/tutor...

You can now Run DeepSeek-V3-0324 locally using our 1.58 & 2.51-bit Dynamic GGUF!🐋 We shrank the 720GB model to 131GB (-80%) by selectively quantizing layers for the best performance. Fixes basic quants breaking & bad output issues: www.unsloth.ai/blog/deepsee... GGUF huggingface.co/unsloth/Deep...

We teamed up with 🤗Hugging Face to release a free notebook for fine-tuning Gemma 3 with GRPO Learn to: • Enable reasoning in Gemma 3 (1B) • Prepare/understand reward functions • Make GRPO work for tiny LLMs Notebook: colab.research.google.com/github/unslo... Details: huggingface.co/reasoning-co...

We made a Guide to teach you how to Fine-tune LLMs correctly! Learn about: • Choosing the right parameters & training method • RL, GRPO, DPO, CPT • Data prep, Overfitting, Evaluation • Training with Unsloth & deploy on vLLM, Ollama, Open WebUI 🔗https://docs.unsloth.ai/get-started/fine-tuning-guide

Tutorial: Train your own Reasoning LLM for free! Make Llama 3.1 (8B) have chain-of-thought with DeepSeek's GRPO algorithm. Unsloth enables 90% less VRAM use. Learn about: • Reward Functions + dataset prep • Training on free Colab GPUs • Running + Evaluating Guide: docs.unsloth.ai/basics/reaso...

Today, we’re launching new algorithms that enable 10x longer context lengths & 90% less VRAM for training Reasoning Models (GRPO). Using Unsloth, you can now train your own reasoning model with just 5GB VRAM for Qwen2.5-1.5B with no accuracy loss. Blog: unsloth.ai/blog/grpo

You can now reproduce DeepSeek-R1's reasoning on your own local device! Experience the "Aha" moment with just 7GB VRAM. Unsloth reduces GRPO training memory use by 80%. 15GB VRAM can transform Llama-3.1 (8B) & Phi-4 (14B) into reasoning models. Blog: unsloth.ai/blog/r1-reas...

You can finetune Phi-4 for free on Colab now! Unsloth finetunes LLMs 2x faster, with 70% less VRAM, 12x longer context - with no accuracy loss Documentation: docs.unsloth.ai We also fixed 4 bugs in Phi-4: unsloth.ai/blog/phi4 Phi-4 Colab: colab.research.google.com/github/unslo...

Introducing Unsloth Dynamic 4-bit Quantization! Naive quantization often harms accuracy but we avoid quantizing certain parameters. This achieves higher accuracy using only <10% more VRAM than BnB 4bit Read our Blog: unsloth.ai/blog/dynamic... Quants on Hugging Face: huggingface.co/collections/...

You can finetune Llama-3.2-Vision-11B for free on Colab now! Unsloth finetunes VLMs 2x faster, with 50% less VRAM, 6x longer context - with no accuracy loss. Other VLMs like Qwen2-VL, Llava & Pixtral are supported. Blog: unsloth.ai/blog/vision Fine-tuning Colabs: docs.unsloth.ai/get-started/...