Erik Scholz

Green-Sky

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Green-Sky

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new activity about 7 hours ago

empero-ai/Qwen3.8-35B-A3B-Distill:🚩 Report: Spam

liked a model about 7 hours ago

empero-ai/Qwen3.8-35B-A3B-Distill

reacted to eaddario's post with 🔥 2 days ago

Experimental global target bits‑per‑weight quantization of **XHToken/Spark-X2.5-1.7B** and **XHToken/Spark-X2.5-4B**. Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global 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, GPQA, MMLU, etc.) and methodology in the model's card. https://huggingface.co/eaddario/Spark-X2.5-1.7B-GGUF https://huggingface.co/eaddario/Spark-X2.5-4B-GGUF

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Green-Sky 's datasets 3

Green-Sky/mmlu-redux-2.0-for-llama.cpp

Preview • Updated Apr 10 • 512

Green-Sky/mmlu-redux-for-llama.cpp

Preview • Updated Apr 10 • 204

Green-Sky/LongBench-v2-for-llama.cpp

Viewer • Updated Apr 9 • 503 • 30