Hugging Face's logo
Aptronym's picture
10 6 406

Aptronym

Aptronym
McEmbola's profile picture molbal's profile picture RicardoEstep's profile picture
·

AI & ML interests

Emergent Gaming

Recent Activity

new activity 3 days ago
ManniX-ITA/gemma-4-A4B-98e-v3-it:Scripts
liked a dataset 3 days ago
ConicCat/MiniC2_V3.2
reacted to eaddario's post with 👍 3 days ago
Experimental global target bits‑per‑weight quantization of Qwen/Qwen3.5-4B and Qwen/Qwen3.5-9B 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/Qwen3.5-4B-GGUF https://huggingface.co/eaddario/Qwen3.5-9B-GGUF
View all activity

Organizations

None yet