Unsloth v0.1.915-beta

Unsloth is an open-source AI platform for running and training open models locally. It combines Unsloth Studio, a graphical interface for running and training models, with Unsloth Core, a code-based library for fine-tuning and machine learning workflows. It supports language, vision, audio, embedding, and diffusion models across Windows, Linux, WSL, and macOS.

The project is particularly focused on making model fine-tuning more efficient by reducing memory usage and improving training performance. It can also run local models and connect them to coding agents and other tools.

Features

Unsloth provides a graphical environment for downloading, running, and training open models. Users can search for models, run them locally, fine-tune them, and export the resulting models to formats such as GGUF and safetensors.

Key features include:

• Local LLM inference

• LLM fine-tuning

• Vision and multimodal models

• Audio models

• Embedding models

• Diffusion models

• GGUF support

• MLX support

• LoRA and other fine-tuning workflows

• Model export

• RAG

• Web search

• Tool calling

• Code execution

• MCP support

• Multi-GPU support

• Claude Code integration

• OpenAI Codex integration

• OpenCode integration

• Docker support

• Self-hosted web interface

• Python API and notebooks

Unsloth Start can connect local models to supported AI coding agents, allowing tools such as Claude Code and Codex to use models running through Unsloth.

The platform also supports several hardware backends. The project lists NVIDIA, AMD, Intel, CPU, and Vulkan support, although capabilities differ depending on the hardware and workload.

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Unsloth v0.1.915-beta for Windows Unsloth-Desktop-Windows.exe | 20.66 MB Unsloth-Desktop-Windows-ARM64.exe | 20.04 MB
Unsloth v0.1.915-beta for macOS Unsloth-Desktop-MacOS.dmg | 21.17 MB
Unsloth v0.1.915-beta for Linux Unsloth-Desktop-Ubuntu.deb | 22.54 MB Unsloth-Desktop-Ubuntu-ARM64.deb | 22.28 MB Unsloth-Desktop-Linux.AppImage | 169.56 MB
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Unsloth v0.1.915-beta Source Code Unsloth v0.1.915-beta Source code (zip) Unsloth v0.1.915-beta Source code (tar.gz)
Unsloth v0.1.915-beta Release Notes: This release introduces Qwen3.8-Flash-Next, polishes the Glass themes (now in Beta), adds a Composer position slider and lets models interact with files in the built-in browser. It also brings lower idle memory usage, preserved chat context when switching GGUF models, faster MLX loading and FP8 LoRA training. Highlights Qwen3.8-Flash-Next Dynamic V3.1 - 10-15% extra accuracy Guide Glass theme improvements across pages, menus, the sidebar and browser Browser file interaction lets models click, read and edit HTML, files and pages Lower idle memory with faster MLX loading and reloading Preserved chat context when switching between GGUF models FP8 LoRA training with lower GPU memory usage Composer position slider to place the chat box anywhere on new chats Glass and appearance Set the chat box position, from top to bottom, in Settings > Appearance > Composer. Glass themes now have softer highlights, lighter shadows and improved frosted effects across menus, panels and the browser. Pages like Library, Images and Train no longer heavily blur background images. Open conversations now slightly dim the background and use denser glass for the composer. Windows and Linux title bars better match Glass themes and background images. Settings changes made in one browser tab now sync across other open tabs. Built-in browser With Browser use enabled, models can click and type in HTML and React pages created in chat. Models can read and edit HTML, React, Markdown, code and text files directly in the browser. Back restores the previous version. Models can also read PDFs and other documents open in the browser. Download prompts can remember permissions per site or across all sites, including for program files. Chat Chats now show activity between tool calls, including context compaction on GGUF models. Attach saved Library files using + > More > Attach from Library. Switching away from a GGUF model and back now preserves chat context, avoiding the need to re-read long histories. GGUF models can read up to four web pages at once when tools run without asking. Connections and API OpenRouter models now load live, with working Reload models support. Improved compatibility with newer MCP servers. The Decision API adds LiquidAI's d1 models and Decider 2B (MLX only). Settings > API > Quickstart now includes curl, Python and JavaScript examples. Performance and media Unsloth no longer loads PyTorch and other ML libraries at startup, significantly reducing idle memory usage. Configure startup features in Settings > General > Startup performance. MLX models load and reload much faster, and reducing a loaded model's context now takes seconds. Long prompts on supported MLX models use less memory. SDXL renders faster with very limited VRAM, including 4 GB setups. Image LoRAs in your model folders now appear on the Images page. Training Faster, lower-memory LoRA training on 16-bit bases with recent NVIDIA GPUs using float8_training = "fp8_lora". DPO, ORPO, KTO, GRPO and CPO training now run on MLX. Fixed crashes when full fine-tuning Gemma 4 in float16. Trim overlapping highlight bulletsCombine related Glass bullets What's Changed Bump install.sh / install.ps1 pins to unsloth>=2026.11.1 by @danielhanchen in #16554 Sync the pad id to FastModel's fallback tokenizer by @danielhanchen in #16557 Studio: faster MLX load and reload, ready to serve when loaded by @Lyxot in #16536 Parallel env tests: pin UNSLOTH_RETURN_LOGITS unset for the packing checks by @danielhanchen in #16594 Tray status test: end the listener slice at its own close by @danielhanchen in #16600 Studio: let managed accounts run terminal and Python tools on macOS by @NilayYadav in #16579 Studio: fit reasoning_effort to a template's enforced allowlist by @Lyxot in #16527 Kernels: name every tl.constexpr parameter with a trailing comment by @danielhanchen in #16585 FP8 LoRA: fp8 forward and input gradient on the frozen 16-bit base by @danielhanchen in #16423 Make startup ML warmup opt-in by feature by @Imagineer99 in #16469 Kernels: keep per-call sizes out of the Triton compile key (layernorm dW, fp8 block matmul, nvfp4 split sum) by @danielhanchen in #16584 Studio: stop browser panel pages typing stray control keys on macOS by @NilayYadav in #16583 fix(studio): price MLX attention on the route the runtime takes by @Lyxot in #16517 Studio diffusion kernels: keep per-resolution sizes out of the Triton compile key by @danielhanchen in #16582 Scan baseline: re-review the five unsloth-zoo sites 2026.11.1 moved by @danielhanchen in #16624 Context parallelism: generic Ulysses on any Hugging Face attention model, per-instance trainer wrappers by @danielhanchen in #16558 Hide the sidebar footer fade only in glass themes by @Imagineer99 in #16572 Fix duplicated wallpaper titlebar tint and modal dimming by @Imagineer99 in #16571 Studio: fix external-server disconnects and show the server port by @oobabooga in #16595 Fix glass surfaces in the built-in browser by @Imagineer99 in #16570 FP8 rowwise scaled_mm: keep fp16 output in range before torch 2.14, and drop the row guard for always-dequant dtypes when compiled by @danielhanchen in #16598 Multi-LoRA: export expert LoRA in the layout vanilla PEFT loads by @danielhanchen in #16568 Studio: load OpenRouter models from the API by @NilayYadav in #16562 Studio: read several web pages at once on local models by @NilayYadav in #16489 Studio: keep profile and appearance changes made in another window by @NilayYadav in #16580 Studio: scale spreadsheet figures in thousands or millions with decimals by @NilayYadav in #16590 Studio: keep the MCP tool list steady between messages by @NilayYadav in #16488 Studio: stop showing a cancelled tool request reply as finished by @NilayYadav in #16473 Studio: keep chat context when switching models by @NilayYadav in #16493 Studio: do not download PyTorch Clef for an over-long decision request by @NilayYadav in #16509 Backend CI: run tensor and pipeline parallel gloo suites in separate shards by @danielhanchen in #16634 Studio: keep image follow-ups running after the tab closes by @NilayYadav in #16491 Studio: attach saved Library files to the current chat by @danielhanchen in #16522 Forward the model argument through update_weight_conversions overrides (transformers 5.21+) by @danielhanchen in #16559 Studio: keep long replies with escaped math streaming smoothly by @NilayYadav in #16490 Studio: keep turn bookmarks made in another window by @NilayYadav in #16578 fix(studio): reject ambiguous S3 audio references by @Max-Reisinger in #16529 Studio: keep chat settings changed in another window by @NilayYadav in #16589 Studio: list third-party image LoRAs from model folders by @NilayYadav in #16483 Studio: ask Laya questions without instructions by their id by @NilayYadav in #16496 Studio: open Try after Use in Decision API for Clef and LLM fine-tunes by @NilayYadav in #16497 Studio: show status between tool rounds and while context compaction runs by @oobabooga in #16608 Studio: let Laya fine-tunes answer from their GGUF export by @NilayYadav in #16511 Studio: keep indentation in sent messages by @NilayYadav in #16495 Studio: keep non-English text in structured Laya instructions by @NilayYadav in #16510 Studio: move to mcp 2 and fastmcp 4 by @NilayYadav in #16475 Studio: keep custom spellings when another window saves voice settings by @NilayYadav in #16577 Studio: show how to call the Decision API from code by @NilayYadav in #16499 Studio diffusion: show whether group offload keeps the transformer on the GPU or streams it every step by @oobabooga in #16610 Studio: show the newest Kimi models in the model picker by @NilayYadav in #16591 MLX: run DPO, ORPO, KTO, GRPO and CPO through trl, and stop blocking SFT arguments zoo honors by @Lyxot in #16461 Studio: report bad decision datasets before the model downloads by @NilayYadav in #16498 Studio: LiquidAI d1 and Decider decision models, train LFM3 into d1 by @Lyxot in #16519 Studio: size streamed SDXL UNet leaves from the denoise peak and lend them to the VAE decode by @danielhanchen in #16620 Diffusion tests: pin the NVIDIA-host spoof and assert ROCm keeps the stock path by @danielhanchen in #16609 Studio: glass theme polish and a composer position slider by @shimmyshimmer in #16643 Studio: show Run settings gear on every GGUF quant row by @shimmyshimmer in #16660 Label the Glass theme as BETA by @Imagineer99 in #16642 Studio: let browser use read, click and edit files in the browser panel by @shimmyshimmer in #16644 Studio: remember download answers for a site or every site, programs included by @shimmyshimmer in #16658 fix(studio): load the MLX training dataset before the model by @Lyxot in #16618 Join the Windows glass sidebar and title bar over chat wallpaper by @Imagineer99 in #16641 Block compile: checkpoint recompute reuses the forward graph (shared_kv_states, output collector) by @danielhanchen in #16596 Studio: keep native tool calls in mixed ShareGPT chats and render null-content calls on Qwen3 by @danielhanchen in #16120 Studio: check the weight index of a module path that only reads as the root by @danielhanchen in #16212 feat(studio): price GLM-5.3-Flash oQ quants as unsloth-zoo loads them by @Lyxot in #16619 Improve model-load error guidance and shorten failure notifications by @Imagineer99 in #16540 Block Swap blocker: Qwen3.8-Flash-Next-FP8 can't train with offload_layers, its FP8 n-gram table loads without its weight_scale by @LeoBorcherding in #15632 Legacy CLI: apply --chunk_size and --stride to a text file passed as --dataset by @vineethsaivs in #16424 Model picker contracts: read the GGUF row and gear pins from variantSelectMeta by @danielhanchen in #16669 Studio H3: release resident denoiser blocks' host copies and give the RAM to the conditioner by @danielhanchen in #16639 Studio video: load a commit-pinned cached snapshot offline when refs/main is missing by @danielhanchen in #16640 cuDNN SDPA: keep known-bad decode and NaN-dQ shapes off cuDNN before torch 2.14 / 2.15 by @danielhanchen in #15934 Studio: let search_conversation find this turn's results on API providers by @danielhanchen in #16666 Studio: MLX training from a saved adapter, Continued Pretraining, streamed and audio datasets, with exact resume by @Lyxot in #16622 fix(studio): rewrite S3 audio paths in single-record JSON manifests by @Max-Reisinger in #16631 Apply appearance contrast once per slider drag by @Imagineer99 in #16676 Confine Windows glass wallpaper blur to the inner page pane by @Imagineer99 in #16671 fix(studio): skip hidden HTML subtrees in RAG parsing by @LimbC-C in #16612 Fused preference loss: fall back when a global forward hook is registered by @lonexreb in #15226 Studio: remember thinking Off after eject by @Souravrajvi0 in #15093 Studio i18n: translate the browser tool steps and program download prompts in every locale by @danielhanchen in #16677 Studio: forward a request's other chat_template_kwargs to llama-server by @debojyoti-dev in #16567 Studio: saving a preset no longer brings it back on the model it was tuned on by @roydonsequeira in #14603 New Contributors @LimbC-C made their first contribution in #16612 Full Changelog: v0.1.914-beta...v0.1.915-beta

Performance and Compatibility

Performance is one of Unsloth's main purposes. Its training optimizations are designed to reduce memory requirements and speed up fine-tuning compared with conventional training workflows.

Actual performance depends heavily on the model, quantization, GPU, available VRAM, dataset, batch size, and training configuration. The project supports multi-GPU setups and provides specialized guidance for newer NVIDIA hardware, AMD GPUs, Intel GPUs, and Apple silicon.

Unsloth Studio can run on Windows, Linux, WSL, and macOS. The project also provides native desktop packages for Windows, macOS, and Linux, while the Studio interface can be installed separately and accessed through a local web interface.

macOS users can run models through MLX and GGUF, while supported Apple Silicon systems can also perform training workflows. NVIDIA GPUs provide the broadest support for training workloads, while AMD and Intel support varies by backend and feature.

Because Unsloth works with local models, storage requirements can become significant. Model weights, datasets, checkpoints, caches, and exported models can consume substantially more storage than the application itself.

System Requirements

Unsloth does not have one fixed hardware requirement because different workloads have very different resource requirements.

Supported platforms include:

• Windows

• Linux

• WSL

• macOS

• NVIDIA GPUs

• AMD GPUs

• Intel GPUs

• Apple Silicon

• CPU-only operation for supported workloads

For the code-based installation, the current documentation uses Python 3.13 with uv for Linux, WSL, and Windows installations.

GPU training requires a compatible backend and sufficient memory for the selected model and training configuration. Larger models generally require more VRAM or system memory, while quantized models can substantially reduce the memory requirement.

Docker is also supported, with official images available for different environments and GPU configurations.

Pros and Cons

Pros

• Free and open source

• Local model inference

• Fine-tuning support

• Memory-efficient training optimizations

• Supports multiple model types

• GGUF support

• MLX support

• RAG capabilities

• MCP support

• Tool calling

• Code execution

• Multi-GPU support

• Windows, Linux, WSL, and macOS support

• NVIDIA, AMD, Intel, and CPU support

• Apple Silicon support

• Docker support

• Desktop application

• Web interface

• Python-based workflows

• Supports AI coding agents

Cons

• Hardware requirements vary significantly by model

• Large models require substantial VRAM or system memory

• Training workflows can be technically complex

• Different hardware backends do not provide identical capabilities

• Model files and training datasets can consume considerable storage

• Some advanced workflows require command-line or Python knowledge

How to Install

The simplest option is Unsloth Desktop. The project provides native packages for Windows, macOS, and Linux. Linux users can choose DEB or AppImage packages, while macOS and Windows have dedicated installers.

For Unsloth Studio, macOS, Linux, and WSL can use:

curl -fsSL https://unsloth.ai/install.sh | sh

Windows can use:

irm https://unsloth.ai/install.ps1 | iex

After installation, start the Studio interface with:

unsloth studio

The interface runs locally and can be accessed through a web browser.

For developers who prefer the Python package, Unsloth Core can be installed inside a virtual environment. The project currently recommends using uv to create the environment and install Unsloth with automatic PyTorch backend selection.

Docker is another option for users who prefer an isolated environment or need a reproducible setup.

Final Verdict

Unsloth is more than a fine-tuning library. Its current ecosystem combines local model inference, training, model conversion, RAG, tool calling, AI agents, and a graphical Studio interface into a single open-source platform.

Its biggest strength is flexibility. Users can start with the desktop application and local models, then move to Python-based fine-tuning or Docker when they need more control. Support for multiple hardware platforms also makes it useful across a wider range of systems, although the available capabilities vary between backends.

The main limitation is complexity. Unsloth is aimed at users working with local AI models rather than people looking for a simple chatbot. Model size, VRAM, training configuration, and hardware compatibility all have a significant impact on the experience.

Free

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Software Informations:
Developer:
Unsloth
Operating System:
Windows / macOS / Linux
Date Added:
2026-11-05T03:07:14.982Z
Categories:

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