Unsloth
AI & LLMsFine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.
Release History
View all versions →v0.1.804-beta38 fixes35 featuresUnsloth now supports running Qwen3.8-Flash-Next and GLM-5.3-Flash locally with significant performance improvements and reduced memory requirements. This release also brings numerous chat, reliability, and model management enhancements.
v0.1.803-beta15 fixes16 featuresThis bug fix release introduces significant improvements including experimental auto compaction for longer chats, preview of remote & LAN access, and enhanced chat/hardware/API functionalities. It also includes over 170 PRs addressing bugs, reliability, and performance.
v0.1.802-beta17 fixes13 featuresThis bug fix release introduces significant improvements including experimental auto compaction for longer chats, preview of remote & LAN access, and enhanced hardware support with Intel XPU. It also resolves numerous bugs across MLX, AMD, and UI components.
v0.1.801-beta20 fixes18 featuresThis release introduces experimental Auto Compaction for longer chats and a preview of Remote & LAN Access. It also brings significant speed improvements to chat, support for custom llama.cpp builds, and enhanced features for the Unsloth Dynamic GGUFs.
v0.1.800-betaBreaking25 fixes35 featuresUnsloth now supports running Qwen3.8 models locally with reduced RAM requirements and offers significant performance improvements, including faster inference for GGUFs and MiniMax-H3. New features enhance chat capabilities, tool integration, and hardware compatibility.
v0.1.71-beta3 fixes4 featuresThis release improves the media picker by offering only compatible artifacts based on host capabilities and clarifies H3 speed differences. It also fixes issues with artifact filtering and search result relevance.
v0.1.702-beta6 fixes20 featuresUnsloth Desktop is a new open-source application for running and training AI models locally on Windows, macOS, and Linux. This release introduces features like tunable VRAM usage, faster inference, improved hardware support, and enhanced tool calling capabilities.
v0.1.701-beta4 fixes13 featuresUnsloth Desktop is released, a new app for running and training AI models locally on various platforms and hardware. It introduces features like enhanced tool calling, support for new models, faster inference, no-code training, and remote deployment.
v0.1.70-beta13 featuresUnsloth Desktop is released, offering a local AI model development environment for Windows, macOS, and Linux. It features enhanced tool calling, support for new models, faster inference, no-code training, and broad hardware compatibility.
v0.1.62-betav0.1.61-beta48 fixes5 featuresThis release introduces Meta's Muse Glimmer 30B open model and adds video generation and preliminary image diffusion support. It also includes numerous bug fixes and UI enhancements for the Unsloth Studio and Desktop applications.
v0.1.60-beta48 fixes4 featuresThis release introduces Meta's Muse Glimmer 30B model, optimized for local agentic and coding workflows, and enhances Unsloth's Studio and Desktop applications with numerous bug fixes and UI improvements.
desktop-v0.1.527-betaUpdated installation scripts to pin dependencies for unsloth and unsloth-zoo to specific versions.
v0.1.527-beta53 fixes4 featuresThis release includes numerous Studio and Desktop improvements, bug fixes, and feature enhancements. Key updates involve better handling of cached pipelines, improved UI elements in Studio, and more robust desktop application behavior.
desktop-v0.1.526-beta1 fixUpdated installation scripts to pin unsloth and unsloth-zoo to specific newer versions. This ensures users install compatible versions of these dependencies.
v0.1.523-betav0.1.521-beta31 fixes12 featuresThis release introduces support for Kimi K3 and DeepSeek v4 Flash models running locally with Unsloth Dynamic GGUFs. It also includes significant improvements to download efficiency, installation processes across different operating systems, and numerous bug fixes for the Unsloth Studio and backend.
v0.1.526-beta34 fixes12 featuresUnsloth now supports running Kimi K3 and DeepSeek v4 Flash models locally with Dynamic GGUFs, featuring improved download efficiency and DSpark support for faster inference. This update also includes numerous bug fixes, installation improvements, and enhanced studio features.
v0.1.52-beta30 fixes13 featuresThis release introduces support for running Kimi K3 and DeepSeek v4 Flash models locally with Unsloth Dynamic GGUFs, featuring improved download efficiency and robustness. It also includes numerous bug fixes and enhancements across the Unsloth Studio and installation processes.
v0.1.525-beta33 fixes12 featuresThis release introduces local support for Kimi K3 and DeepSeek v4 Flash models via Unsloth Dynamic GGUFs, along with significant improvements in download efficiency and installation robustness across various systems and operating systems. It also includes numerous bug fixes and enhancements to the Unsloth Studio and desktop applications.
v0.1.524-beta34 fixes10 featuresThis release introduces local support for Kimi K3 and DeepSeek v4 Flash models via Unsloth Dynamic GGUFs, along with significant improvements to download efficiency and system memory handling. It also includes numerous bug fixes and studio enhancements.
v0.1.522-beta31 fixes12 featuresThis release introduces support for running Kimi K3 and DeepSeek v4 Flash models locally with Unsloth Dynamic GGUFs, featuring improved download efficiency and robustness. It also includes numerous bug fixes and enhancements across the Unsloth Studio and installation processes.
v0.1.511-beta16 fixes14 featuresThis release introduces support for Kimi K3 and DeepSeek v4 Flash with Unsloth Dynamic GGUFs, enables parallel chat generation, and adds a Deep Research mode. It also brings significant improvements to AMD and Intel GPU support, DoRA training, and various installer, MLX, export, and inference fixes.
v0.1.51-beta27 fixes11 featuresThis release introduces Kimi K3 local execution, parallel chat capabilities, and a Deep Research mode. It also brings significant improvements to AMD and Intel GPU support, DoRA training, and various installer, MLX, export, and inference fixes.
v0.1.512-beta14 fixes11 featuresThis release introduces local support for Kimi K3 and DeepSeek v4 Flash models via Unsloth Dynamic GGUFs, alongside parallel chat capabilities and a new Deep Research mode. It also brings significant improvements to AMD and Intel GPU support, DoRA training, and various installer, MLX, export, and inference fixes.
Common Errors
OutOfMemoryError5 reportsOutOfMemoryError typically arises when the model and training data exceed available GPU memory. Reduce batch size, use gradient accumulation, enable CPU offloading via `unsloth.utils.force_low_cpu_mem` if possible, or switch to a smaller model to decrease memory footprint and prevent the error. Consider upgrading your GPU or using distributed training across multiple GPUs if the problem persists.
CalledProcessError3 reports"CalledProcessError" in unsloth often arises when external processes invoked during GGUF conversion, like `llama.cpp` functions, fail due to insufficient system resources (RAM or disk space) or incorrect file paths. To fix this, ensure you have ample free RAM and disk space before converting, and carefully double-check that all file paths specified in your arguments, especially the model path, exist and are correct. Try reducing batch sizes or the number of threads used to lower memory requirements if resource limitations persist.
TorchRuntimeError3 reportsTorchRuntimeError in Unsloth often arises from incorrect tensor shapes, mismatched data types or unexpected values during operations, particularly in custom CUDA kernels. Ensure input tensors to Unsloth's optimized functions have the expected dimensions and data types as defined in the function signatures or kernel implementations. Verify that no NaN or Inf values are present in the tensors as these can propagate and cause failures inside kernels.
JSONDecodeError2 reportsJSONDecodeError typically arises from malformed JSON data, such as incomplete files, incorrect syntax, or unexpected characters like Byte Order Marks (BOMs). To fix it, ensure model index and configuration files are valid JSON, free of syntax errors and extraneous characters. Verify file integrity and, if necessary, re-download or regenerate the problematic JSON files.
UnicodeDecodeError2 reportsUnicodeDecodeError in unsloth often arises when attempting to load a model or its index file with incorrect encoding, frequently due to corrupted or non-standard UTF-8 data. To fix this, ensure your model files are properly encoded as UTF-8, and if reading from local files, explicitly specify the encoding as 'utf-8' during file operations to prevent misinterpretation.
ModuleNotFoundError2 reportsThe "ModuleNotFoundError" in Unsloth usually arises from missing required packages not installed alongside the core library, or from incorrect installation paths. Resolve this by first ensuring you've installed Unsloth via pip (`pip install unsloth`) including any necessary dependencies using `pip install "unsloth[extra_dependencies]"`, replacing `extra_dependencies` with the actual relevant dependency group if available (e.g., `deepspeed`, `gpu_monitoring`). If issues persist, double-check your Python environment and PYTHONPATH variable to ensure Unsloth's installation directory is correctly included.
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