---
license: mit
tags:
  - benchmark-results
  - quantization
  - vision-language
  - mlx
---

# lfm2vl-lab — raw evaluation results

Every number behind [`lfm2vl-lab`](https://github.com/shubhambaid/lfm2vl-lab), a
component-wise quantization and edge-inference study of
[`LiquidAI/LFM2.5-VL-3B`](https://huggingface.co/LiquidAI/LFM2.5-VL-3B) on Apple Silicon
(M4 Pro, 24 GB).

Published separately from the code so the numbers stay citable and diffable on their own.
Weights in that study are ephemeral by design — converted, evaluated, recorded, deleted.
These JSON files are what survives.

## Files

| file | what it holds |
|---|---|
| `coverage_audit_gs64.json` | per-module quantizability audit at group size 64 |
| `coverage_audit_by_group_size.json` | the same at group size 32 / 64 / 128 |
| `uniform4_coverage_verified.json` | coverage read back from saved weights |
| `published_quant_survey.json` | 56 published LFM2-VL/LFM2.5-VL quant repos, checked for vision-tower quantization |
| `token_budget_sweep.json` | accuracy and latency vs `max_image_tokens`, 3 tasks × 4 budgets |
| `baseline_bf16_n200_t512.json` | bf16 baseline vs Liquid's published figures |
| `sweep_component_sensitivity.json` | 17 quantization configs × 5 benchmarks |
| `head_to_head_n300.json` | 5 decision-relevant configs at n=300 with 95% CIs |

## Headline findings

**The vision token budget matters more than quantization.** ScreenSpot-v2 moves from
53.0 to 79.0 by changing `max_image_tokens` from the default 256 to 512 — and falls
back to 70.0 at 1024. Non-monotonic, and the optimum is task-dependent (512 for screens
and documents, 256 for natural images).

**Component sensitivity is flat.** Across 17 configs, nothing measurably degrades at 6 or
8 bits on any component. The only clear loser is uniform 4-bit, the one config pushing
the vision tower to 4 bits — lower on 5 of 5 benchmarks.

**A third of the vision tower cannot be quantized.** SigLIP2 shape-optimized has
`intermediate_size = 4304` (16 × 269, 269 prime), so all 27 `mlp.fc2` modules are
skipped at every standard group size — silently, with no warning.

**No published quant touches the vision tower.** Of 56 repos surveyed, including
LiquidAI's own MLX releases, none has a `vision_tower` quantization entry.

## Caveats

n is 100–300 per benchmark, so 95% CIs run ±4 to ±5 points. Differences smaller than
that are not claims. Single machine, single seed (0), `mlx-vlm==0.6.13`.