# unslothai/unsloth

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66,628 stars · 5,974 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/unslothai/unsloth
- Homepage: https://unsloth.ai/docs
- awesome-repositories: https://awesome-repositories.com/repository/unslothai-unsloth.md

## Topics

`agent` `deepseek` `deepseek-r1` `fine-tuning` `gemma` `gemma3` `gpt-oss` `llama` `llama3` `llm` `llms` `mistral` `openai` `qwen` `qwen3` `reinforcement-learning` `text-to-speech` `tts` `unsloth` `voice-cloning`

## Description

Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware.

The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fine-tuning, while offering a unified web-based interface for no-code model training, data preparation, and real-time performance monitoring.

Beyond its core training capabilities, the project includes a local inference runtime that supports API-based deployment, tool-calling, and automated output verification. It manages the entire model development process, from dataset generation and hyperparameter configuration to model exporting and performance benchmarking across diverse hardware configurations.

The software provides setup utilities for local development environments and includes diagnostic tools to assist with installation and hardware compatibility.

## Tags

### Artificial Intelligence & ML

- [Language Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/model-fine-tuning-adaptation/language-model-training.md) — Accelerates large language model training through memory-efficient techniques and optimized processing workflows. ([source](https://unsloth.ai/docs))
- [LLM Fine-Tuning Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/training-systems/model-training-engines/llm-fine-tuning-engines.md) — Optimizes memory usage and compute speed for fine-tuning large language models on consumer-grade hardware.
- [Multimodal Training Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/integrated-development-platforms/machine-learning-platforms/multimodal-training-platforms.md) — Supports efficient fine-tuning of text, vision, and audio models through optimized kernels and specialized data pipelines.
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Streamlines the adaptation of existing models for specific tasks while maintaining low hardware requirements. ([source](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide))
- [Training Acceleration Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/training-acceleration-engines.md) — Integrates high-throughput engines into the training stack to enable simultaneous fine-tuning and fast inference with lower memory requirements. ([source](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide))
- [Efficient Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/efficient-training-pipelines.md) — Reduces memory usage and increases processing speed during the fine-tuning of large models for specific applications.
- [No-Code Training Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/no-code-training-interfaces.md) — Configures training for text, vision, and audio models through simplified interfaces that bypass manual code implementation. ([source](https://unsloth.ai/docs/new/studio))
- [Model Fine-Tuning and Adaptation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/model-fine-tuning-adaptation.md) — Refines pre-trained models for specific domains by generating response variations and updating weights based on custom reward functions. ([source](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide))
- [Quantized Adapters](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/training-efficiency/quantized-adapters.md) — Applies low-precision weight updates to compressed model layers to enable efficient fine-tuning on consumer-grade hardware.
- [Mixture of Experts Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning/mixture-of-experts-optimizations.md) — Splits adapter parameters in mixture-of-experts models to reduce memory overhead and increase training speed. ([source](https://unsloth.ai/docs/new/faster-moe))
- [Local Model Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/local-ai-deployment-platforms/local-model-execution.md) — Facilitates the local discovery, downloading, and execution of language models through integrated tools and API endpoints. ([source](https://cdn.jsdelivr.net/gh/unslothai/unsloth@main/README.md))
- [Training Backend Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/training-efficiency/training-backend-optimizers.md) — Automatically selects optimal training methods based on detected hardware to maximize efficiency using high-speed kernels. ([source](https://unsloth.ai/docs/new/faster-moe))
- [Local Model Serving](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/agent-and-tool-integrations/api-servers/local-model-serving.md) — Deploys language models on local hardware and exposes them through network API endpoints for external integration.
- [Data Ingestion and Preparation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/data-ingestion-preparation.md) — Structures raw text into organized question-answer pairs and generates synthetic data using local resources. ([source](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide))
- [Training Hyperparameter Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/training-configuration-management/training-hyperparameter-configurations.md) — Adjusts granular training parameters like batch size and learning rate to balance computational throughput against available memory constraints. ([source](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide))
- [Model Comparison Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/machine-learning-evaluation/model-comparison-interfaces.md) — Facilitates side-by-side output comparison by running identical prompts through multiple model versions simultaneously. ([source](https://unsloth.ai/docs/new/studio/chat))
- [Model Inference Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/engines-runtimes-servers/model-inference-servers.md) — Exposes loaded models via command-line API endpoints with built-in authentication for scalable inference services. ([source](https://unsloth.ai/docs/basics/api))
- [Local Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/local-inference-runtimes.md) — Powers local execution of quantized models while enabling API support and tool-calling capabilities for external software.
- [Speculative Decoding Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-optimization/inference-acceleration-techniques/speculative-decoding-strategies.md) — Predicts multiple future tokens in parallel to accelerate the generation process and reduce total processing steps. ([source](https://unsloth.ai/docs/models/qwen3.6))
- [Tool Call Auto-healing](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/runtime-interfaces-orchestration/ai-model-inference-utilities/tool-call-auto-healing.md) — Corrects malformed or syntactically invalid tool calls generated by models to ensure reliable execution. ([source](https://unsloth.ai/docs/new/studio/chat))
- [Context Memory Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/model-fine-tuning-adaptation/context-memory-optimizations.md) — Manages memory usage during training by chunking data across sequences to support extended context lengths. ([source](https://unsloth.ai/docs/new/grpo-long-context))
- [Multimodal Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/model-fine-tuning-adaptation/multimodal-fine-tuning.md) — Adapts vision, audio, and text models to specific datasets using efficient training techniques that maintain high accuracy.
- [Machine Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning.md) — Teaches models advanced reasoning and problem-solving capabilities through reinforcement learning and custom objective functions.
- [Speech Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/fine-tuning-frameworks/speech-model-fine-tuning.md) — Captures unique vocal characteristics and speaking styles by training speech models with highly efficient computational techniques. ([source](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning))
- [Reward Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/objectives-and-optimization/mathematical-training-objectives/reward-functions.md) — Defines custom scoring mechanisms to evaluate outputs and steer the training process toward specific reasoning or formatting targets. ([source](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide))
- [Model Export Formats](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/inference-optimization-utilities/model-export-formats.md) — Exports custom model weights into standard file formats to ensure compatibility with local inference and production systems. ([source](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide))
- [Model Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization.md) — Provides pre-optimized model files featuring enhanced chat templates and tokenization for faster local inference. ([source](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune))
- [Multimodal Input Handlers](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/multimodal-input-handlers.md) — Accepts documents, images, and audio files within chat conversations to provide multimodal context for prompts. ([source](https://unsloth.ai/docs/new/studio/chat))
- [Embedding Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/fine-tuning-frameworks/transfer-learning-techniques/embedding-model-fine-tuning.md) — Efficient fine-tuning techniques enable the optimization of embedding models while maintaining full compatibility with existing data pipelines and encoder architectures. ([source](https://unsloth.ai/docs/new/embedding-finetuning))
- [Vision Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/fine-tuning-frameworks/vision-model-fine-tuning.md) — Selective module training within vision architectures balances high-performance output with improved model accuracy. ([source](https://unsloth.ai/docs/basics/vision-fine-tuning))
- [Training Progress Monitoring](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/utilities/training-progress-monitoring.md) — Real-time tracking of loss, gradient norms, and hardware utilization maintains precise oversight during the entire model development process. ([source](https://unsloth.ai/docs/new/studio))
- [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-evaluation-metrics.md) — Monitors training progress via loss metrics and validates output quality through manual chat sessions or automated test sets. ([source](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide))
- [Model Selection Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-management/model-selection-utilities.md) — Matches model variants to specific hardware constraints and performance requirements for optimized inference. ([source](https://unsloth.ai/docs/models/gemma-4))

### Software Engineering & Architecture

- [Custom Kernel Accelerators](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/computational-efficiency/custom-kernel-accelerators.md) — Executes low-level mathematical operations using hand-optimized kernels to maximize hardware throughput and minimize memory overhead.
- [Computational Graph Optimizers](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/computational-efficiency/computational-graph-optimizers.md) — Rewrites execution paths at runtime to minimize latency and improve processing speed for complex multimodal operations.
- [FP8 Training Optimization](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/computational-efficiency/fp8-training-optimization.md) — Utilizes lower-precision numerical formats to decrease memory usage and boost training throughput. ([source](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning))

### Operating Systems & Systems Programming

- [Gradient Checkpointing](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/process-and-memory-management/memory-management/buffer-and-cache-management/gradient-checkpointing.md) — Lowers peak memory consumption by recomputing intermediate activations during the backward pass instead of storing them.

### DevOps & Infrastructure

- [Model Inference Deployment](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-inference-deployment.md) — Hosts models in local environments with built-in parameter tuning, automated tool invocation, and integrated output evaluation. ([source](https://unsloth.ai/docs))
- [Code Execution Sandboxes](https://awesome-repositories.com/f/devops-infrastructure/execution-environments/code-execution-runtimes/code-execution-sandboxes.md) — Runs isolated Bash and Python scripts to verify model outputs, generate files, and perform computations securely. ([source](https://unsloth.ai/docs/new/studio/chat))

### Part of an Awesome List

- [AI and Agents](https://awesome-repositories.com/f/awesome-lists/ai/ai-and-agents.md) — A library for faster LLM fine-tuning and training with reduced memory usage.
- [Fine-Tuning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/fine-tuning-frameworks.md) — Memory-efficient and high-speed fine-tuning library.
- [Inference Platforms](https://awesome-repositories.com/f/awesome-lists/ai/inference-platforms.md) — Unified web UI for training and running open models locally.
- [Large Language Models](https://awesome-repositories.com/f/awesome-lists/ai/large-language-models.md) — Optimized fine-tuning for faster training and lower memory usage.
- [LLM Development Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/llm-development-frameworks.md) — Python library optimized for fine-tuning large language models.
- [Model Deployment and Platforms](https://awesome-repositories.com/f/awesome-lists/ai/model-deployment-and-platforms.md) — Library for faster and more memory-efficient LLM fine-tuning.
- [Model Serving and Inference](https://awesome-repositories.com/f/awesome-lists/ai/model-serving-and-inference.md) — Web UI for training and running local open models.
- [Model Training](https://awesome-repositories.com/f/awesome-lists/ai/model-training.md) — Fine-tune models with reduced memory usage and faster speeds.
- [Model Training Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/model-training-frameworks.md) — Optimized fine-tuning for popular large language models.
- [Training and Orchestration](https://awesome-repositories.com/f/awesome-lists/devops/training-and-orchestration.md) — Optimized framework for fast LLM fine-tuning and training.
- [Large Language Models (LLMs)](https://awesome-repositories.com/f/awesome-lists/more/large-language-models-llms.md) — Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.

### Data & Databases

- [Data Engineering Pipelines](https://awesome-repositories.com/f/data-databases/data-pipeline-orchestration/data-engineering-pipelines.md) — Connects data blocks for seeding, processing, and validation to build custom datasets for fine-tuning workflows. ([source](https://unsloth.ai/docs/new/studio/data-recipe))
- [Data Collator Pipelines](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-processing/ml-data-pipelines/data-collator-pipelines.md) — Standardizes raw data through dynamic resizing, padding, and masking to ensure consistent batch structures during training.
- [Data Preprocessing Utilities](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-processing/ml-data-pipelines/data-preprocessing-utilities.md) — Structures and generates synthetic training data via visual workflows to improve model learning efficacy.

### Security & Cryptography

- [Authentication Strategies](https://awesome-repositories.com/f/security-cryptography/identity-access-management/authentication-strategies.md) — Generates authentication credentials within settings to secure access to locally running model instances. ([source](https://unsloth.ai/docs/basics/api))

### User Interface & Experience

- [Model Management Dashboards](https://awesome-repositories.com/f/user-interface-experience/graphical-user-interfaces/ai-specific-ux-design/model-management-dashboards.md) — Visual interfaces consolidate model training, data preparation, and chat interactions into a single dashboard for diverse hardware and operating systems. ([source](https://cdn.jsdelivr.net/gh/unslothai/unsloth@main/README.md))