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## News
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* 02/10/2025 [1.9.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.9.0): ⚡ Offload `tokenizer` fixes to [Toke(n)icer](https://github.com/modelcloud/tokenicer) pkg. Optimized `lm_head` quant time and vram usage.
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* 02/12/2025 [1.9.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.9.0): ⚡ Offload `tokenizer` fixes to [Toke(n)icer](https://github.com/modelcloud/tokenicer) pkg. Optimized `lm_head` quant time and vram usage.
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Optimized `DeekSeek v3/R1` model quant vram usage. Fixed `Optimum` compat regresion in `v1.8.1`. 3x speed-up for `Torch` kernel when using Pytorch >= 2.5.0 with `model.compile()`. New `calibration_dataset_concat_size` option to enable calibration data `concat` mode to mimic original GPTQ data packing strategy which may improve quant speed and accuracy for datasets like `wikitext2`.
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* 02/08/2025 [1.8.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.8.1): ⚡ `DeekSeek v3/R1` model support. New flexible weight `packing`: allow quantized weights to be packed to `[int32, int16, int8]` dtypes.
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`Triton` and `Torch` kernels supports full range of new `QuantizeConfig.pack_dtype`.
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