What unslothai/unsloth shipped
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Week of October 5, 2026
What shipped
- Studio can now install and run vLLM on AMD GPUs on Linux and through Windows Subsystem for Linux, instead of requiring an NVIDIA GPU. Pull request #12785
- Studio now installs prebuilt flash-attn, causal-conv1d and mamba-ssm wheels on Windows and ARM instead of skipping them. Pull request #13262
- Prebuilt CUDA 13 kernel wheels for flash-attn, causal-conv1d and mamba-ssm are now built for
win_amd64,linux_aarch64andwin_arm64. Pull request #13261 - GRPO now runs under an FSDP2 config without failing on the first training step with an inference tensor error. Pull request #13156
- An explicit
rope_scalingpassed tofrom_pretrainedis now applied to both the in-process model and vLLM. Pull request #13315 - Packed and padding-free supervised fine-tuning on the transformers modeling path now runs attention through variable-length flash attention. Pull request #13234
- Offloaded embedding tables now stay in host RAM when the Trainer and accelerate place the model. Pull request #13320
- Image generation with torch.compile on AMD is fixed for the training config leak, Windows without Visual Studio and the max tier. Pull request #13227
- New Studio installs on Windows x64 with cu130 and Python 3.13 now get torch 2.13, while existing installs keep their torch. Pull request #13263
Why it matters
This week widened where the project can actually run: vLLM on AMD, prebuilt CUDA 13 kernels for Windows and ARM, and 4-bit loads on Intel XPU go beyond the NVIDIA-only path. Training also got more dependable with GRPO under FSDP2, applied rope_scaling, packed rows on the transformers path and host-RAM embedding offload. Many Studio fixes target users with smaller machines, mixed quantizers and older dependency sets.
Changelog entry
- Studio: install and run vLLM on AMD GPUs on Linux and through WSL on Windows. Pull request #12785
- Studio: install prebuilt flash-attn, causal-conv1d and mamba-ssm wheels on Windows and ARM. Pull request #13262
- Build prebuilt CUDA 13 kernel wheels for
win_amd64,linux_aarch64andwin_arm64. Pull request #13261 - Run GRPO under FSDP2 without an inference tensor error on the first step. Pull request #13156
- Apply an explicit
rope_scalingto the model and to vLLM. Pull request #13315 - Run packed rows on the transformers modeling path through varlen flash attention. Pull request #13234
- Keep offloaded embeddings in host RAM when Trainer and accelerate place the model. Pull request #13320
- Intel XPU: load 4-bit without a C compiler via a PyTorch quantize fallback. Pull request #13309
- Studio: fix torch.compile for image generation on AMD. Pull request #13227
- Studio: gate auth.db and rag.db opens and closes against the SQLite 3.51.0-3.51.1 deadlock. Pull request #13307
- Studio: use torch 2.13 for new Windows x64 cu130 Python 3.13 installs. Pull request #13263
This week: vLLM installs on AMD Linux and WSL, prebuilt CUDA 13 kernel wheels for Windows and ARM, GRPO under FSDP2, rope_scaling actually applied, packed SFT via varlen flash attention, and plenty of Studio fixes for Metal, MIOpen and mixed quants.
This week the project broadened the hardware and training paths you can use. Studio can install and run vLLM on AMD GPUs on Linux and through Windows Subsystem for Linux, and prebuilt CUDA 13 wheels for flash-attn, causal-conv1d and mamba-ssm are now built for win_amd64, linux_aarch64 and win_arm64 and installed on Windows and ARM. Training saw GRPO run under an FSDP2 config, an explicit rope_scaling applied to the model and to vLLM, packed rows on the transformers modeling path routed through variable-length flash attention, and offloaded embeddings kept in host RAM. Studio also gained a run of reliability fixes: Metal fit within GPU wired headroom, torch.compile for AMD image generation, SQLite WAL deadlock gating, MCP call re-runs, and Intel XPU 4-bit loads without a C compiler.
Week of September 28, 2026
What shipped
- Block Swap lets dense models train past VRAM limits by streaming frozen transformer blocks from host RAM, fetching each block onto the card just before use. Pull request #11832
- Studio's Audio page adds a Clone page with shared audio inputs for upload, recording, history clips, and saved voices. Pull request #12602
- Studio's Convert audio page enables voice conversion with A/B comparison using RVC, Seed-VC, and MeanVC2 models. Pull request #12611
- Studio's Separate audio workflow loads HTDemucs and RoFormer models with a synced stem mixer. Pull request #12610
- Studio's Music page shows Song, Sound effect, and Edit modes reported by loaded audio.cpp models. Pull request #12609
- VAE kernel prebuilds, early quantization probes, and single-block FLUX.1 graphs reduce Studio's first-start time by up to 50%. Pull request #12642
- Denoiser block streaming removes host waits during prefetch, cutting per-step time on large models like FLUX.1 on B200 and L4. Pull request #12664
- Studio reduces image load time by 50% through kernel prebuilds and quantization probes. Pull request #12642
Why it matters
This week brings major Studio audio features (Clone, Convert, Separate, Music) alongside deep performance work: Block Swap unblocks larger model training, streaming denoiser improvements cut inference time by significant margins, and first-start optimizations make the tool noticeably faster to use. Hardware-specific fixes for ROCm and Windows AMD also expand device support.
Changelog entry
- Studio: Clone page with shared audio inputs (upload, record, history, saved voices) Pull request #12602
- Studio: Convert audio page for voice conversion with A/B comparison (RVC, Seed-VC, MeanVC2) Pull request #12611
- Studio: Separate audio workflow with synced stem mixer (HTDemucs, RoFormer models) Pull request #12610
- Studio: Music page with Song, Sound effect, and Edit modes Pull request #12609
- Block Swap: Stream frozen transformer blocks from host RAM for training past VRAM limits Pull request #11832
- Studio: 50% faster first-start time via VAE kernel prebuilds and quantization probes Pull request #12642
- Studio: Async block prefetch removes host waits during denoiser streaming Pull request #12664
- Studio: Fused int8 GEMM for block-streamed image denoisers Pull request #12677
- DoRA magnitude now applied in generate() decoding path Pull request #12642
- Activation QAT preserved in fused LoRA MLPs Pull request #12558
- Embedding optimizer state stays 32-bit when
embedding_learning_rateis set Pull request #12458
Studio's audio workflows expand this week: Clone, Convert, Separate, and Music pages now available. Training gets Block Swap to stream transformer blocks from RAM. Image generation accelerates with fused kernels and async prefetch.
Unsloth Studio ships five new audio pages this week—Clone, Convert, Separate, and Music modes—alongside major performance gains. Block Swap enables training past VRAM limits by streaming transformer blocks from host memory. Image generation speeds up 50% on first start through kernel prebuilds and quantization probes. Denoiser streaming removes host waits for large models on B200 and L4.
Week of September 21, 2026
What shipped
- MLX backend now honors
response_formatwith grammar-constrained decoding, matching the GGUF path's behavior. Pull request #10180 - Unsloth now patches TRL 0.29+ experimental trainers (ORPO, CPO, Online DPO, GKD, PPO, and others) that moved out of trl.trainer. Pull request #12097
- Unsloth Desktop Skills dialog can now create, edit, and delete Agent Skills directly from the UI. Pull request #11800
- Studio can read more chat attachment formats including XLSX, XLSM, and PPTX, with the rest handed to the python tool. Pull request #11379
- Studio's chat attachment interface now shows cards with colored kind icons and names instead of plain text. Pull request #12001
- Unsloth Desktop no longer writes 200+ KB to disk every 2 seconds while idle by marking API reads no-store in HTTP cache control. Pull request #12148
- Studio's GPUs picker now displays how much of the model each GPU gets, not just which cards are used. Pull request #12015
- MLX serving now decodes several replies at once instead of one at a time through batched decoding. Pull request #10310
- Studio backend now caches and coalesces nvidia-smi reads to reduce latency on multi-GPU systems from hundreds of seconds to under 10. Pull request #11995
- LoRA now trains with compressed-tensors checkpoints that quantize activations (FP8, INT8 W8A8) by passing gradients through fake-quantization. Pull request #11585
Why it matters
This week brought major fixes for TRL 0.29 compatibility, performance improvements on desktop and multi-GPU Studio instances, and new features for attachments and skills in the UI. The batched MLX serving and response_format grammar support on MLX expand what the project can handle without backend routing.
Changelog entry
- MLX backend now respects
response_formatwith grammar-constrained decoding Pull request #10180 - TRL 0.29+ experimental trainers (ORPO, CPO, Online DPO, GKD, PPO, Nash-MD, XPO, BCO, PRM) now receive Unsloth patches Pull request #12097
- Desktop Skills dialog supports creating, editing, and deleting Agent Skills from the UI Pull request #11800
- Studio reads more chat attachment formats (XLSX, XLSM, PPTX) with others routed to python tool Pull request #11379
- Chat attachments display as cards with colored kind icons and file names Pull request #12001
- Desktop app no longer writes 200+ KB to disk every 2 seconds by using no-store HTTP cache control Pull request #12148
- GPUs picker displays how much of the model each GPU receives Pull request #12015
- MLX backend decodes multiple replies simultaneously instead of one at a time Pull request #10310
- Studio caches and coalesces nvidia-smi reads, reducing multi-GPU latency significantly Pull request #11995
- LoRA training now works with compressed-tensors checkpoints using dynamic FP8 and INT8 W8A8 quantization Pull request #11585
- Triton MoE grouped GEMM stays in compiled graphs and indexes weights past 2^31 elements Pull request #12114
- SDXL VAE now decodes in fp16 on fp16 GPUs instead of upcasting to fp32 Pull request #12036
This week: TRL 0.29+ trainer patching, MLX response_format grammar support, Skills creation in Desktop, batched MLX serving, GPU split display in Studio, and faster multi-GPU nvidia-smi caching.
This week's updates focus on expanding inference capabilities and improving user experience across Unsloth Studio and Desktop. Major work includes full TRL 0.29+ support for experimental trainers, MLX grammar-constrained decoding, batched reply serving on MLX, and Desktop Skills management. Studio gains GPU split visualization and dramatic performance improvements on multi-GPU systems through nvidia-smi caching. Chat attachments now display as formatted cards with better file format support.