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Datasets:
shubhambaid
/
lfm2vl-lab-results

Tags:
benchmark-results
quantization
vision-language
mlx
License:
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Raw evaluation results: coverage audits, token budget sweep, 17-config sensitivity sweep, n=300 head-to-head

Browse files
Files changed (9) hide show
  1. README.md +55 -0
  2. baseline_bf16_n200_t512.json +83 -0
  3. coverage_audit_by_group_size.json +119 -0
  4. coverage_audit_gs64.json +2707 -0
  5. head_to_head_n300.json +643 -0
  6. published_quant_survey.json +838 -0
  7. sweep_component_sensitivity.json +2532 -0
  8. token_budget_sweep.json +194 -0
  9. uniform4_coverage_verified.json +58 -0
README.md ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: mit
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+ tags:
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+ - benchmark-results
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+ - quantization
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+ - vision-language
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+ - mlx
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+ ---
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+
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+ # lfm2vl-lab — raw evaluation results
11
+
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+ Every number behind [`lfm2vl-lab`](https://github.com/shubhambaid/lfm2vl-lab), a
13
+ component-wise quantization and edge-inference study of
14
+ [`LiquidAI/LFM2.5-VL-3B`](https://huggingface.co/LiquidAI/LFM2.5-VL-3B) on Apple Silicon
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+ (M4 Pro, 24 GB).
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+
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+ Published separately from the code so the numbers stay citable and diffable on their own.
18
+ Weights in that study are ephemeral by design — converted, evaluated, recorded, deleted.
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+ These JSON files are what survives.
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+
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+ ## Files
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+
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+ | file | what it holds |
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+ |---|---|
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+ | `coverage_audit_gs64.json` | per-module quantizability audit at group size 64 |
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+ | `coverage_audit_by_group_size.json` | the same at group size 32 / 64 / 128 |
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+ | `uniform4_coverage_verified.json` | coverage read back from saved weights |
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+ | `published_quant_survey.json` | 56 published LFM2-VL/LFM2.5-VL quant repos, checked for vision-tower quantization |
29
+ | `token_budget_sweep.json` | accuracy and latency vs `max_image_tokens`, 3 tasks × 4 budgets |
30
+ | `baseline_bf16_n200_t512.json` | bf16 baseline vs Liquid's published figures |
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+ | `sweep_component_sensitivity.json` | 17 quantization configs × 5 benchmarks |
32
+ | `head_to_head_n300.json` | 5 decision-relevant configs at n=300 with 95% CIs |
33
+
34
+ ## Headline findings
35
+
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+ **The vision token budget matters more than quantization.** ScreenSpot-v2 moves from
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+ 53.0 to 79.0 by changing `max_image_tokens` from the default 256 to 512 — and falls
38
+ back to 70.0 at 1024. Non-monotonic, and the optimum is task-dependent (512 for screens
39
+ and documents, 256 for natural images).
40
+
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+ **Component sensitivity is flat.** Across 17 configs, nothing measurably degrades at 6 or
42
+ 8 bits on any component. The only clear loser is uniform 4-bit, the one config pushing
43
+ the vision tower to 4 bits — lower on 5 of 5 benchmarks.
44
+
45
+ **A third of the vision tower cannot be quantized.** SigLIP2 shape-optimized has
46
+ `intermediate_size = 4304` (16 × 269, 269 prime), so all 27 `mlp.fc2` modules are
47
+ skipped at every standard group size — silently, with no warning.
48
+
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+ **No published quant touches the vision tower.** Of 56 repos surveyed, including
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+ LiquidAI's own MLX releases, none has a `vision_tower` quantization entry.
51
+
52
+ ## Caveats
53
+
54
+ n is 100–300 per benchmark, so 95% CIs run ±4 to ±5 points. Differences smaller than
55
+ that are not claims. Single machine, single seed (0), `mlx-vlm==0.6.13`.
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