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Inkling-MLX-REAP12-4bit

Image-Text-to-Text
MLX
Safetensors
inkling_mm_model
Mixture of Experts
multimodal
inkling
thinking-machines
reap
pruned
audio-text-to-text
conversational
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  1. README.md +25 -21
  2. model-00001-of-00082.safetensors +1 -1
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README.md CHANGED
@@ -12,6 +12,7 @@ tags:
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  - thinking-machines
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  - reap
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  - pruned
 
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  ---
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  # Inkling-MLX-REAP12-4bit
@@ -21,7 +22,7 @@ tags:
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  A **REAP-pruned**, 4-bit MLX build of
22
  [thinkingmachines/Inkling](https://huggingface.co/thinkingmachines/Inkling):
23
  each MoE layer keeps its **225 highest-saliency routed experts** (of 256), a
24
- **12% expert prune**. Free lunch — text and vision intact.
25
 
26
  **Code / loader:** [github.com/PipeNetwork/inkling-mlx](https://github.com/PipeNetwork/inkling-mlx)
27
 
@@ -36,31 +37,34 @@ embeddings are untouched.** Inkling routes **very uniformly** (routing entropy 0
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  only ~1 cold expert per layer under multimodal calibration), so it is only *lightly*
37
  prunable — reflected below.
38
 
39
- ## Calibrated on text **and images** (this matters)
40
 
41
  Inkling is multimodal, and expert saliency was profiled over a mixed corpus of **text
42
- (code + 15 languages + reasoning) and 200 real images** run through the full vision
43
- path. This is deliberate: an earlier **text-only** calibration pruned experts that
44
- ground *visual* features (they rarely fire on text), which quietly wrecked image
45
- understanding — a photo of a Pallas's cat was described as a *"brown bear"*, a golf
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- ball as a *"butterfly"* — while text perplexity looked fine. Adding images to the
47
- calibration restores it: on a held-out image-ID test this build scores **6/6** vs
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- **2/6** for the text-only-calibrated version, at no extra text cost.
 
 
49
 
50
  ## Measured quality (4-bit)
51
 
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- | Build | Experts kept | Size | Text perplexity | vs unpruned | Vision (held-out image ID) |
53
- |---|---:|---:|---:|---:|---:|
54
- | [Inkling-MLX-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-4bit) (unpruned) | 256 | ~490 GB | 3.830 | — | ✓ |
55
- | [Inkling-MLX-REAP12-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-REAP12-4bit) | 225 | ~470 GB | 3.794 | -0.9% | 6/6 |
56
- | [Inkling-MLX-REAP25-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-REAP25-4bit) | 192 | ~402 GB | 3.918 | +2.3% | 6/6 |
57
- | [Inkling-MLX-REAP50-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-REAP50-4bit) | 128 | ~272 GB | 4.681 | +22.2% | ~6/6 |
58
-
59
- This build: **text perplexity 3.794 (-0.9% vs the unpruned 4-bit)**, **vision 6/6**
60
- on the held-out image-ID set, 96.4% of router-weighted expert contribution retained.
61
- Pruning is applied to the already-quantized build; because expert subsetting is along the
62
- expert axis and affine-quant groups run along the hidden axis, it is **bit-identical to
63
- pruning the bf16 source then requantizing**.
 
64
 
65
  ## ⚠️ Loading requires the bundled `inkling_mlx` loader
66
 
 
12
  - thinking-machines
13
  - reap
14
  - pruned
15
+ - audio-text-to-text
16
  ---
17
 
18
  # Inkling-MLX-REAP12-4bit
 
22
  A **REAP-pruned**, 4-bit MLX build of
23
  [thinkingmachines/Inkling](https://huggingface.co/thinkingmachines/Inkling):
24
  each MoE layer keeps its **225 highest-saliency routed experts** (of 256), a
25
+ **12% expert prune**. Free lunch — text, vision and audio intact.
26
 
27
  **Code / loader:** [github.com/PipeNetwork/inkling-mlx](https://github.com/PipeNetwork/inkling-mlx)
28
 
 
37
  only ~1 cold expert per layer under multimodal calibration), so it is only *lightly*
38
  prunable — reflected below.
39
 
40
+ ## Calibrated on text, images **and audio** (this matters)
41
 
42
  Inkling is multimodal, and expert saliency was profiled over a mixed corpus of **text
43
+ (code + 15 languages + reasoning), 200 real images, and 180 speech clips** run through
44
+ the full vision and audio paths. This is deliberate: a **text-only** calibration prunes
45
+ experts that ground *visual* features (a Pallas's cat → *"brown bear"*, a golf ball →
46
+ *"butterfly"*); adding only text+image then leaves *audio*-grounding experts unprotected
47
+ (speech transcription word-overlap fell from 0.88 to 0.57 at 25% pruning) — all while
48
+ text perplexity looked fine the whole time. Profiling over all three modalities keeps
49
+ every expert that matters to any of them. On held-out tests this build scores **vision
50
+ 6/6** (vs 2/6 text-only) and **audio 0.88** overlap (vs 0.57 text+image), at no extra
51
+ text cost.
52
 
53
  ## Measured quality (4-bit)
54
 
55
+ | Build | Experts kept | Size | Text ppl | vs unpruned | Vision (image ID) | Audio (speech overlap) |
56
+ |---|---:|---:|---:|---:|---:|---:|
57
+ | [Inkling-MLX-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-4bit) (unpruned) | 256 | ~490 GB | 3.830 | — | ✓ | ✓ |
58
+ | [Inkling-MLX-REAP12-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-REAP12-4bit) | 225 | ~470 GB | 3.806 | -0.6% | 6/6 | 0.88 |
59
+ | [Inkling-MLX-REAP25-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-REAP25-4bit) | 192 | ~402 GB | 3.946 | +3.0% | 6/6 | 0.87 |
60
+ | [Inkling-MLX-REAP50-4bit](https://huggingface.co/pipenetwork/Inkling-MLX-REAP50-4bit) | 128 | ~272 GB | 4.682 | +22.2% | 5/6 | 0.87 |
61
+
62
+ This build: **text perplexity 3.806 (-0.6% vs the unpruned 4-bit)**, **vision 6/6**
63
+ (held-out image ID), **audio 0.88** (held-out speech transcription word-overlap),
64
+ 96.2% of router-weighted expert contribution retained. Pruning is applied to the
65
+ already-quantized build; because expert subsetting is along the expert axis and
66
+ affine-quant groups run along the hidden axis, it is **bit-identical to pruning the bf16
67
+ source then requantizing**.
68
 
69
  ## ⚠️ Loading requires the bundled `inkling_mlx` loader
70
 
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