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docs/source/en/quantization/bitsandbytes.md
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[LLM.int8()](https://hf.co/papers/2208.07339) is a quantization method that aims to make large language model inference more accessible without significant degradation. Unlike naive 8-bit quantization, which can result in loss of critical information and accuracy, LLM.int8() dynamically adapts to ensure sensitive components of the computation retain higher precision when needed.
QLoRA, or 4-bit quantization, compresses a model even further to 4-bits and inserts a small set of trainable low-rank adaptation (LoRA) weights to allowing training.
QLoRA, or 4-bit quantization, compresses a model even further to 4-bits and inserts a small set of trainable low-rank adaptation (LoRA) weights to allowing training.
> **Note:** For a user-friendly quantization experience, you can use the `bitsandbytes` [community space](https://huggingface.co/spaces/bnb-community/bnb-my-repo).
Run the command below to install bitsandbytes.

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docs/source/en/quantization/overview.md
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If you are new to quantization, we recommend checking out these beginner-friendly quantization courses in collaboration with DeepLearning.AI.
* [Quantization Fundamentals with Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/)
* [Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth
* [Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth)
## User-Friendly Quantization Tools
If you are looking for a user-friendly quantization experience, you can use the following community spaces and notebooks:
* [Bitsandbytes Space](https://huggingface.co/spaces/bnb-community/bnb-my-repo)
* [GGUF Space](https://huggingface.co/spaces/ggml-org/gguf-my-repo)
* [MLX Space](https://huggingface.co/spaces/mlx-community/mlx-my-repo)
* [AuoQuant Notebook](https://colab.research.google.com/drive/1b6nqC7UZVt8bx4MksX7s656GXPM-eWw4?usp=sharing#scrollTo=ZC9Nsr9u5WhN)