Large language model (LLMs) Tutorials | Unsloth Documentation
Introducing Unsloth Desktop: the first local app to run & train models
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For the complete documentation index, see llms.txt. This page is also available as Markdown.

Large language model (LLMs) Tutorials

Discover the latest LLMs and learn how to run and fine-tune models locally for optimal performance with Unsloth.

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MiniMax-M3.1-Flash

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DeepSeek-V4.1-Flash

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Qwen3.8

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Qwen3.8-Flash-Next

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GLM-5.3-Flash

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Meta Muse Spark Open

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Qwen-Image-2.1

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EmbeddingGemma 2

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GLM-5.3

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Meta Muse Glimmer

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DeepSeek-V4-Pro-0813

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DeepSeek-V4-0731

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GLM-5.3

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Kimi K3

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GLM-5.2

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Unsloth NVFP4

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MTP Guide

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DeepSeek-V4

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Unsloth Start

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DiffusionGemma

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Inkling-Small

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Inkling

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Unsloth for AMD

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Kimi K2.7 Code

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Gemma 4 QAT

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MiniMax M3

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Qwen3.6 MTP

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Qwen3.6

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Gemma 4

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Unsloth API endpoint

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NVIDIA Nemotron 3 Ultra

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Nemotron 3 Nano Omni

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Kimi K2.6

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Qwen3.5

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Mistral 3.5

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GLM-5.1

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Granite 4.1

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MiniMax-2.7

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Unsloth Studio: Run + train LLMs

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NVIDIA-Nemotron-3

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GLM-5

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Claude Code

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OpenAI Codex

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MiniMax-2.5

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Qwen3-Coder-Next

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Kimi K2.5

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DeepSeek-OCR 2

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GLM-4.7-Flash

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Qwen-Image-2512

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Nemotron 3

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FunctionGemma

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Qwen3-VL

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DeepSeek-OCR

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Run gpt-oss

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Ministral 3

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Kimi K2 Thinking

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Vision Reinforcement Learning

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DeepSeek-V3.1 Terminus

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Qwen3 Coder

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Magistral 1.2

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Gemma 3n

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Qwen3-2507

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DeepSeek-R1-0528

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Kimi K2

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Devstral 2507

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Fine-tune on Blackwell & RTX 50 GPUs

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TTS Fine-tuning

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Qwen3

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Phi-4 reasoning

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Dynamic 2.0 GGUFs

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Llama 4

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DeepSeek-V3-0324

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Grok 2

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Gemma 3

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QwQ-32B

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DeepSeek-R1

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Reinforcement Learning (RL)

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Mistral Small 3.1

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Llama 3

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Vision Fine-tuning

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Continued Pretraining

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Llama 3.3

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Gemma 2

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Phi-3

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