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Nov 2, 2025
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Update latest news section in README.md #2166

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6 changes: 1 addition & 5 deletions README.md
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</p>

## Latest News
* 11/1/2025 5.1.0-dev: ✨Brumby (attention free) model support.
* 10/31/2025 5.1.0-dev: ✨IBM Granite Nano support. New `calibration_concat_separator` config option.
* 10/30/2025 5.1.0-dev: 🎉AWQ support out of beta with full feature support in including multi-gpu quant and MoE vram saving.
* 10/30/2025 5.1.0-dev: ✨Marin model. New AWQ Torch reference kernel. Fix AWQ Marlin kernel for bf16. Fix GLM 4.5/4.6 MoE missing `mtp` layers on model save (HF bug). Modular refractor.
* 10/28/2025 5.1.0-dev: Minimax M2 support with [ModelCloud BF16 M2 Model](https://huggingface.co/ModelCloud/MiniMax-M2-BF16). New `VramStrategy.Balanced` quantization property for reduced memory usage for large MoE on multi-3090 (24GB) devices.
* 11/3/2025 [5.2.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v5.2.0): 🎉Minimax M2 support with [ModelCloud BF16 M2 Model](https://huggingface.co/ModelCloud/MiniMax-M2-BF16). New `VramStrategy.Balanced` quantization property for reduced memory usage for large MoE on multi-3090 (24GB) devices. ✨Marin model. New AWQ Torch reference kernel. Fix AWQ Marlin kernel for bf16. Fix GLM 4.5/4.6 MoE missing `mtp` layers on model save (HF bug). Modular refractor. 🎉AWQ support out of beta with full feature support in including multi-gpu quant and MoE vram saving. ✨Brumby (attention free) model support. ✨Brumby (attention free) model support. ✨IBM Granite Nano support. New `calibration_concat_separator` config option.
* 10/24/2025 [5.0.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v5.0.0): 🎉 Data-parallel quant support for `MoE` models on multi-gpu using `nogil` Python. `offload_to_disk` support enabled by
default to massively reduce `cpu` ram usage. New `Intel` and `AMD` cpu hw accelerated `TorchFused` kernel. Packing stage is now 4x faster and now inlined with quantization. `Vram` pressure for large models reduced during quantization.
`act_group_aware` is 16k+ times faster and now the default when `desc_act=False` for higher quality recovery without inference penalty of `desc_act=True`. New beta quality `AWQ` support with full `gemm`,
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