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unsloth

Free

Web UI for training and running open models like Gemma 4, Qwen3.6, DeepSeek, gpt-oss locally.

Model APIsFreeFree tier
#fine-tuning#llm-training#lora#qlora#gpu-efficient#open-source#huggingface#model-training#rlhf#quantization
Inputs: text, image, audioOutputs: text, image, audio
Type
Open Source
Company
Unsloth
Visit Website GitHub (63.5k stars)

About unsloth

Unsloth is an open-source UI that enables users to run and train AI models on local hardware, supporting over 500 models including vision, text-to-speech, embeddings, and reinforcement learning. It achieves 2x faster training with up to 60% less VRAM (free version) through hand-derived backward passes and custom quantization kernels, without sacrificing model quality. Features include fine-tuning (LoRA, QLoRA, full), preference alignment (DPO, ORPO, RLHF), auto-dataset creation from PDF/CSV/DOCX, visual data editing, live training monitoring, and inference capabilities such as self-healing tool calling and agent connectivity (Claude Code, Codex). Models can be exported to GGUF, Ollama, vLLM, and HuggingFace formats. The free plan works on a single T4 GPU and includes one-click Colab notebooks.

Key Features

2x faster training via hand-derived backward passes replacing PyTorch autograd
80% less memory through custom 4-bit quantization and gradient checkpointing kernels
Zero quality degradation — matches HuggingFace trainer results on benchmarks
Supports LoRA, QLoRA, and full fine-tuning out of the box
DPO, ORPO, and RLHF training for preference alignment
One-click Colab notebooks for popular models and tasks
Exports to GGUF, Ollama, vLLM, and HuggingFace formats
Works with Llama 3, Mistral, Qwen 2.5, Gemma 2, Phi-3, and 50+ model families
Runs on a single T4 GPU — no A100 or multi-GPU setup required
Integrates directly with HuggingFace datasets and model hub

Pros & Cons

Pros
  • 2x faster training compared to standard HuggingFace training
  • Significant VRAM reduction (60% free, up to 90% enterprise)
  • No accuracy loss — matches baseline benchmarks
  • Supports a vast range of models (500+) across multiple modalities
  • Open-source and free to use with one-click Colab notebooks
  • Easy integration with agents and export to multiple formats
  • Active community and frequent updates (new models within days of release)
Cons
  • Multi-GPU support is limited in the free plan (coming soon)
  • Free plan VRAM reduction is lower (60%) compared to Pro (80%) or Enterprise (90%)
  • Advanced features like multi-node training require enterprise plan
  • Requires local hardware with compatible GPU (e.g., NVIDIA, AMD, Intel) and some setup

Best For

ML engineers: Fine-tune a chat model on domain-specific conversationsDevelopers: Adapt a base model for code generation in a specific languageAI researchers: Preference alignment with DPO or RLHF on human feedback dataStartups: Train a small specialized model on limited hardware budgetStudents: Rapid prototyping of fine-tuned models for proof-of-concept demos

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