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* start integration * fix * add and debug tests * update tests * make pytorch serialization works * compatible with device_map and offload * fix tests * make style * add ref * guard against safetensors * add float8 and style * fix is_serializable * Fix shard_checkpoint compatibility with quanto * more tests * docs * adjust memory * better * style * pass tests * Update src/transformers/modeling_utils.py Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * add is_safe_serialization instead * Update src/transformers/quantizers/quantizer_quanto.py Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * add QbitsTensor tests * fix tests * simplify activation list * Update docs/source/en/quantization.md Co-authored-by: David Corvoysier <david.corvoysier@gmail.com> * better comment * Update tests/quantization/quanto_integration/test_quanto.py Co-authored-by: David Corvoysier <david.corvoysier@gmail.com> * Update tests/quantization/quanto_integration/test_quanto.py Co-authored-by: David Corvoysier <david.corvoysier@gmail.com> * find and fix edge case * Update docs/source/en/quantization.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * pass weights_only_kwarg instead * fix shard_checkpoint loading * simplify update_missing_keys * Update tests/quantization/quanto_integration/test_quanto.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * recursion to get all tensors * block serialization * skip serialization tests * fix * change by cuda:0 for now * fix regression * update device_map * fix doc * add noteboon * update torch_dtype * update doc * typo * typo * remove comm --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: David Corvoysier <david.corvoysier@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Younes Belkada <younesbelkada@gmail.com>
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Quantization
Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes.
Quantization techniques that aren't supported in Transformers can be added with the [HfQuantizer] class.
Learn how to quantize models in the Quantization guide.
QuantoConfig
autodoc QuantoConfig
AqlmConfig
autodoc AqlmConfig
AwqConfig
autodoc AwqConfig
GPTQConfig
autodoc GPTQConfig
BitsAndBytesConfig
autodoc BitsAndBytesConfig
HfQuantizer
autodoc quantizers.base.HfQuantizer