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reacted to eaddario's post with 🤗 about 16 hours ago
Experimental global target bits‑per‑weight quantization of allenai/Olmo-3-7B-Instruct and allenai/Olmo-3-7B-Think 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/Olmo-3-7B-Instruct-GGUF https://huggingface.co/eaddario/Olmo-3-7B-Think-GGUF
liked a dataset about 16 hours ago
nbeerbower/hemlock-sft-v0.2
upvoted a paper 1 day ago
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