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title: Local Model Explorer + quant picks
emoji: 🎯
colorFrom: indigo
colorTo: blue
sdk: static
pinned: false
tags:
- gguf
- llama-cpp
- local-llm
Local Model Explorer + quant picks
Same idea as codelion's Local Model Explorer: pick your hardware, get the models that fit. This clone goes one further — every model also gets its exact best quant, ranked by measured quality instead of size alone.
Fit + catalog data load live from the original explorer's API. Quant ranking
comes from quant_scores.json: KL-divergence vs full precision over 150
GGUFs (LFM2.5-1.2B, Qwen3.5-0.8B, gemma-3-1b-it). KV precision comes from
kv_scores.json: 12 K/V pairs benched on 3 models (KL vs f16 @4k, PPL +
VRAM @64k), wrapped client-side around the upstream f16-only API with
q5_0/q4_1 preselected. Method in quant-bench
SUMMARY.md / PRIORITY.md.
Rules: single auto pick per model — best measured quality fully resident
on your hardware, no band caps (if Q8_0 fits, it wins). Dominated quants
(Q4_0, Q4_1, …) lose to their better twin when it's in the same repo.
Logic in rankQuants.js (mirrors rank_quants.py).