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71.2
TFLOPS
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DBME
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I do optimized EXL2 quants for 48GB VRAM setups.
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DBMe/Behemoth-123B-v1.2-2.85bpw-h6-exl2
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DBMe/Monstral-123B-v2-2.85bpw-h6-exl2
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DBMe/Endurance-100B-v1-3.6bpw-h6-exl2
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DBMe/Behemoth-123B-v1.2-2.85bpw-h6-exl2
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DBMe/Endurance-100B-v1-3.6bpw-h6-exl2
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DBMe/EVA-Qwen2.5-72B-v0.2-4.86bpw-h6-exl2
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DBMe/EVA-Qwen2.5-72B-v0.2-4.48bpw-h6-exl2
3 months ago
Update README.md
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#1 opened 3 months ago by
bullerwins
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DBMe/EVA-Qwen2.5-72B-v0.2-4.48bpw-h6-exl2
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DBMe/Evathene-v1.0-4.86bpw-h6-exl2
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DBMe/Monstral-123B-2.85bpw-h6-exl2
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DBMe/Mistral-Large-Instruct-2411-2.86bpw-h6-exl2
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DBMe/EVA-Qwen2.5-72B-v0.1-4.86bpw-h6-exl2
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DBMe/EVA-Qwen2.5-72B-v0.0-4.48bpw-h6-exl2
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DBMe/Midnight-Miqu-103B-v1.0-3.53bpw-h6-exl2
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DBMe/Midnight-Miqu-103B-v1.0-3.35bpw-h6-exl2
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DBMe/magnum-v4-72b-4.47bpw-h6-exl2
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DBMe/Mistral-Large-Instruct-2407-2.85bpw-h6-exl2
6 months ago
Set PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync
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#1 opened 6 months ago by
John198
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6 months ago
DBMe/magnum-v4-72b-4.85bpw-h6-exl2
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DBMe/magnum-v4-123b-2.85bpw-h6-exl2
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