@DanKornas: Fine-tuning a new model shouldn’t mean rebuilding the same training notebook from scratch. Unsloth Notebooks is a colle…

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Summary

Unsloth Notebooks is a collection of 250+ open-source fine-tuning and reinforcement-learning notebooks for text, vision, audio, embeddings, and speech models, providing ready-to-use Colab notebooks with data preparation, training, and inference steps.

Fine-tuning a new model shouldn’t mean rebuilding the same training notebook from scratch. Unsloth Notebooks is a collection of 250+ fine-tuning and reinforcement-learning notebooks for AI builders working with text, vision, audio, embeddings, and speech models. It helps you get from model selection to a working experiment by providing model-specific Colab notebooks with data preparation, training, and inference steps. Key features: • Model coverage – examples for Llama, Qwen, Gemma, Mistral, GPT-OSS, and more. • Training workflows – fine-tuning plus GRPO, DPO, ORPO, and other reinforcement-learning setups. • Multimodal tasks – notebooks for vision, audio, TTS, speech-to-text, OCR, and embeddings. • Colab access – open individual notebooks directly from the README’s model-organized tables. • Practical workflow – the Llama 3.1 Alpaca example covers installation, data prep, training, inference, saving, and GGUF conversion. It’s open-source (LGPL-3.0 license). Link in the reply
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Cached at: 07/22/26, 06:23 AM

Fine-tuning a new model shouldn’t mean rebuilding the same training notebook from scratch.

Unsloth Notebooks is a collection of 250+ fine-tuning and reinforcement-learning notebooks for AI builders working with text, vision, audio, embeddings, and speech models.

It helps you get from model selection to a working experiment by providing model-specific Colab notebooks with data preparation, training, and inference steps.

Key features: • Model coverage – examples for Llama, Qwen, Gemma, Mistral, GPT-OSS, and more. • Training workflows – fine-tuning plus GRPO, DPO, ORPO, and other reinforcement-learning setups. • Multimodal tasks – notebooks for vision, audio, TTS, speech-to-text, OCR, and embeddings. • Colab access – open individual notebooks directly from the README’s model-organized tables. • Practical workflow – the Llama 3.1 Alpaca example covers installation, data prep, training, inference, saving, and GGUF conversion.

It’s open-source (LGPL-3.0 license).

Link in the reply

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