AI Briefingπ€ Transformers Supported Quantization Methods Guide
Key point
Compares the characteristics of the bitsandbytes and auto-gptq quantization methods natively supported by the π€ Transformers library.
Details
Quantization is used to run inference on large models on small devices or to fine-tune adapters on top of a quantized model.
The main quantization methods currently natively supported by π€ Transformers are as follows:
- bitsandbytes: Supports model inference and efficient fine-tuning.
- auto-gptq: Focuses on optimizing the model's inference speed.
For reference, the π€ Optimum library also supports various quantization methods, but this content focuses on the native support features within the Transformers library. Also, this information currently only applies to PyTorch models, and TensorFlow and Flax/JAX models are excluded from the scope of support.
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