Baixar Unsloth 0.1.902-beta per a Windows

This release brings faster navigation, shareable run settings, and clearer errors to Unsloth Desktop. It also 4x speeds up Laya decisions, expands hosted Decision API support, and keeps NVFP4, INT4, and MXFP4 checkpoints in 4-bit during LoRA training.
Highlights
- Command palette on Cmd/Ctrl+P. Also, you can now share your GGUF model's run settings
- Laya decisions up to 4.1x faster, plus connections to TypeSafe and other hosted providers
- Clearer errors that explain failed loads and generations, with a View logs button
- NVFP4 LoRA with 45% lower peak memory on Qwen3.8-27B-NVFP4
- INT4 + MXFP4 checkpoints stay packed during LoRA, including Kimi-K2.7-Code and gpt-oss
Desktop + chat
- Cmd/Ctrl+P opens a command palette, and GGUF run settings can be shared as a link or saved without loading.
- Continue response extends a finished reply, and Fix with the model puts HTML canvas errors in the message box.
- A warning flags models that only partly fit on the GPU, and the Images gallery loads thumbnails first.
- The desktop app adds Cmd/Ctrl + and - zoom, Windows 11 style buttons on Windows and Linux, and a check before Update stops training.
Lower-memory checkpoint training
Pre-quantized 4-bit checkpoints now train on their exact published weights. Training reads them packed, so a loaded model needs about as much memory as its 4-bit checkpoint.
- Qwen3.8-27B-NVFP4 peaks at 40.2 GB instead of 72.9 GB on one RTX PRO 6000, with 11% shorter steps.
- Dense compressed-tensors INT4 checkpoints train as published on NVIDIA GPUs. Qwen3-8B w4a16 peaks at 8.5 GB, not 18.8 GB, on a B200.
- Kimi-K2.7-Code trains on 6 B200s with its INT4 experts packed, where a 16-bit copy would need about 2 TB.
- gpt-oss loaded with load_in_4bit = False keeps its MXFP4 experts, so gpt-oss-120b trains on one B200.
How it works
- Triton kernels dequantize each layer on the fly, bit for bit matching compressed-tensors, and backward keeps only the packed weights.
- INT4 stays exact instead of being re-quantized to NF4, which rounds every weight twice and raised Qwen3-8B perplexity by 1.6%.
- For inference-only gpt-oss, UNSLOTH_MXFP4_KEEP_PACKED=0 restores the native load, which prefills faster.
Decision API + Laya
Laya decision models now answer short requests up to 4.1x faster, and hosted decision models work too.
- Add TypeSafe or another hosted decision provider under Connections, then pick its models in Settings > API.
- With the Decision API on, enable Unsloth Decisions in the chat's MCP menu for a decide tool.
- On a B200, a 3-question guardrail check takes 2.8 ms instead of 11.6 ms. Long inputs run at about the same speed.
- Laya loads in about 9 s instead of 17 s and needs about half the peak host RAM.
Image + video
- Image models keep INT8 or FP8 when offloading. On a 10 GiB-capped B200, Z-Image takes 2.3 s, not 38.4 s.
- Qwen-Image-2.1 renders 1536x1536 images up to 3.6x faster on a Radeon 8060S.
Training + models
- Fine-tune T5, T5Gemma, BART, Marian and other text encoder-decoders with FastModel.
- The new unsloth eval command runs lm-evaluation-harness tasks on a checkpoint or LoRA adapter.
- Gemma 4 31B training and batched generation now work on models split across GPUs.
AMD + Apple Silicon
- On Windows, RX 5000 through RX 9000 cards get PyTorch from AMD's multi-arch index.
- MLX moves to mlx 0.32.3 and mlx-vlm 0.7.4, and quantized-KV vision loads can batch replies.
- On a Mac, export LoRA adapters as PEFT or GGUF for GPU machines.