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Merged
merged 2 commits into from
Oct 30, 2025
Merged

Marin scores #2141

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324086a
update marin scores
Qubitium Oct 30, 2025
83a6ef6
update news
Qubitium Oct 30, 2025
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -17,6 +17,7 @@
</p>

## Latest News
* 10/30/2025 5.1.0-dev: +Marin model. +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.
* 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.
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11 changes: 10 additions & 1 deletion tests/models/test_marin.py
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class TestMarin(ModelTest):
NATIVE_MODEL_ID = "/monster/data/model/marin-32b-base"
VRAM_STRATEGY = VRAMStrategy.BALANCED
# VRAM_STRATEGY = VRAMStrategy.BALANCED
# Marin inherits Qwen3's backbone with QK-Norm attention.
EVAL_TASKS = {
EVAL.LM_EVAL.ARC_CHALLENGE: {
"acc": {"value": 0.5725, "floor_pct": 0.04},
"acc_norm": {"value": 0.6007, "floor_pct": 0.04},
},
EVAL.LM_EVAL.MMLU_STEM: {
"acc": {"value": 0.6670, "floor_pct": 0.04},
},
}

def test_marin_module_tree(self):
config = AutoConfig.from_pretrained(self.NATIVE_MODEL_ID, trust_remote_code=True)
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