---
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](https://huggingface.co/spaces/LocalLLaMA/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](https://github.com/)
`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`).