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fix typo #1320

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Feb 21, 2025
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fix typo #1320

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4 changes: 2 additions & 2 deletions README.md
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## News
* 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.
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.optimize()`. 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`.
* 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.
Optimized `DeepSeek 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.optimize()`. 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`.
* 02/08/2025 [1.8.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.8.1): ⚡ `DeepSeek v3/R1` model support. New flexible weight `packing`: allow quantized weights to be packed to `[int32, int16, int8]` dtypes.
`Triton` and `Torch` kernels supports full range of new `QuantizeConfig.pack_dtype`.
New `auto_gc: bool` control in `quantize()` which can reduce quantization time for small model with no chance of oom.
New `GPTQModel.push_to_hub()` api for easy quant model upload to HF repo. New `buffered_fwd: bool` control in `model.quantize()`. Over 50% quantization speed-up for visual (vl) models.
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