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Oct 23, 2025
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note bitblas update #2102

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4 changes: 3 additions & 1 deletion README.md
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* 10/23/2025 [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`,
`gemm_fast`, `marlin` kernel support. `LFM`, `Ling`, `Qwen3 Omni` model support. Quantization is now faster with reduced vram usage. Enhanced logging support with `LogBar`.
`gemm_fast`, `marlin` kernel support. `LFM`, `Ling`, `Qwen3 Omni` model support.
`Bitblas` kernel updated to support Bitblas `0.1.0.post1` reelase.
Quantization is now faster with reduced vram usage. Enhanced logging support with `LogBar`.
* 09/16/2025 [4.2.5](https://github.com/ModelCloud/GPTQModel/releases/tag/v4.2.5): `hyb_act` renamed to `act_group_aware`. Removed finicky `torch` import within `setup.py`. Packing bug fix and prebuilt Pytorch 2.8 whls.
* 09/12/2025 [4.2.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v4.2.0): ✨ New Models Support: Qwen3-Next, Apertus, Kimi K2, Klear, FastLLM, Nemotron H. New `fail_safe` `boolean` toggle to `.quantize()` to patch-fix non-activated `MoE` modules due to highly uneven MoE model training. Fixed LavaQwen2 compat. Patch fix GIL=0 cuda error for multi-gpu. Fix compat with autoround + new transformers.
* 09/04/2025 [4.1.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v4.1.0): ✨ Meituan LongCat Flash Chat, Llama 4, GPT-OSS (BF16), and GLM-4.5-Air support. New experiemental `mock_quantization` config to skip complex computational code paths during quantization to accelerate model quant testing.
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