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https://huggingface.co/CreativityCompound

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liked a Space about 14 hours ago
JimmyK300/Qwen2.5-0.5B-Instruct-GGUF-q2_k
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WCNegentropy/WrinkleBrane
reacted to eaddario's post with ❤️ 14 days ago
Experimental global target bits‑per‑weight quantization of ServiceNow-AI/Apriel-1.6-15b-Thinker and zai-org/GLM-4.6V-Flash Unlike standard llama.cpp quantizations that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global file size target. Key Advantages: - VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM). - Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs. Full benchmarks (PPL, KLD, ARC, MMLU, etc.) and methodology in the models' cards https://huggingface.co/eaddario/Apriel-1.6-15b-Thinker-GGUF https://huggingface.co/eaddario/GLM-4.6V-Flash-GGUF
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