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Sep 12, 2025
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prepare for 4.2 release #1785

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Qubitium Sep 11, 2025
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</div>
<h1 align="center">GPT-QModel</h1>
</p>
<p align="center">LLM model compression/quantization toolkit with hw acceleration support for Nvidia CUDA, AMD ROCm, Intel XPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.</p>
<p align="center">LLM model quantization (compression) toolkit with hw acceleration support for Nvidia CUDA, AMD ROCm, Intel XPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.</p>
<p align="center">
<a href="https://github.com/ModelCloud/GPTQModel/releases" style="text-decoration:none;"><img alt="GitHub release" src="https://img.shields.io/github/release/ModelCloud/GPTQModel.svg"></a>
<a href="https://pypi.org/project/gptqmodel/" style="text-decoration:none;"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gptqmodel"></a>
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</p>

## Latest News
* 09/11/2025 [4.2.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v4.1.0): ✨ New Models Support: 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.
* 09/12/2025 [4.2.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v4.1.0): ✨ 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.
* 08/21/2025 [4.0.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v4.0.0): 🎉 New Group Aware Reordering (GAR) support. New models support: Bytedance Seed-OSS, Baidu Ernie, Huawei PanGu, Gemma3, Xiaomi Mimo, Qwen 3/MoE, Falcon H1, GPT-Neo. Memory leak and multiple model compatibility fixes related to Transformers >= 4.54. Python >= 3.13t free-threading support added with near N x GPU linear scaling for quantization of MoE models and also linear N x Cpu Core scaling of packing stage. Early access Pytorch 2.8 fused-ops on Intel XPU for up to 50% speedup.
* 08/19/2025 4.0.0-dev `main`: Fix quantization memory usage due to some model's incorrect application of `config.use_cache` during inference. Fixed `Transformers` >= 4.54.0 compat which changed layer forward return signature for some models.
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| DeepSeek-V2/V3/R1 | ✅ | GPT-OSS | ✅ | Instella | ✅ | Phi 1-4 | ✅ | XVERSE | ✅ |
| DeepSeek-V2-Lite | ✅ | Granite | ✅ | MiniCPM3 | ✅ | PanGu-α | ✅ | | |
| Dream | ✅ | GRIN-MoE | ✅ | Mistral | ✅ | Qwen 1/2/3 | ✅ | | |
| ERNIE 4.5 | ✅ | Hymba | ✅ | Mixtral | ✅ | Qwen 2/3 MoE | ✅ | | |
| ERNIE 4.5 | ✅ | Hymba | ✅ | Mixtral | ✅ | Qwen 2/3 (Next/MoE) | ✅ | | |

## Platform and HW Support

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