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clef-flash Core ML: 8-bit text decoder (8 parts x 4 buckets) + joint schema head

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README.md ADDED
@@ -0,0 +1,96 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ base_model: Cloudflare/clef-flash
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+ base_model_relation: quantized
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+ pipeline_tag: text-classification
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+ library_name: coreml
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+ tags:
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+ - coreml
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+ - apple
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+ - decision-model
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+ - typed-output
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+ - structured-output
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+ - classification
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+ - clef
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+ - systemone
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+ - qwen3.5
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+ - fluidinference
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+ ---
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+
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+ # clef-flash (Core ML)
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+
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+ Cloudflare's [clef-flash](https://huggingface.co/Cloudflare/clef-flash), a 9B decision model post-trained from
23
+ [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B), converted to Core ML for Apple silicon. **Text only**, 8-bit
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+ weights, runs on the Mac GPU.
25
+
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+ Same contract as Clef and the Jev / SystemOne API: a `state` (text or JSON) and typed questions (`noul` yes/no,
27
+ `choice`, ordered `score`) go in; **one probability for every allowed option of every question** comes out of a
28
+ single forward pass. Nothing is generated.
29
+
30
+ Runtime: `ClefFlashManager` in [FluidUse](https://github.com/FluidInference/FluidUse) (Swift, macOS 15+), with a
31
+ SwiftUI support-ticket triage demo (`ClefFlashDemo`).
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+
33
+ ## Packages
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+
35
+ | File | Role | Size | Precision |
36
+ |---|---|---:|---|
37
+ | `part00` … `part07.mlpackage` | Qwen3.5 decoder, 4 layers each (the last adds the final norm); functions `L256` `L512` `L1024` `L2048` per sequence bucket, weights shared | 6.5 GB total | 8-bit weights (per channel), fp16 compute |
38
+ | `Head.mlpackage` | Clef's joint schema head, fixed shape: ≤ 16 questions, ≤ 96 options; same four functions | 465 MB | fp32 |
39
+ | `embeddings.f16` | input token embeddings (host-side gather) | 2.0 GB | fp16 |
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+ | `output_embeddings.f16` | output embeddings, the head's lexical option rows (clef-flash does not tie them) | 2.0 GB | fp16 |
41
+ | `tokenizer.json`, `config.json` | tokenizer; shapes, buckets and token ids for the host | | |
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+
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+ Each decoder part: `hidden [1, L, 4096]`, `cos` / `sin [L, 64]` (fp16) → `out [1, L, 4096]`. Text-only records use
44
+ positions `0..L-1` on all three M-RoPE axes. The host renders the record exactly like Clef's `encode_record`,
45
+ gathers embeddings, chains the 8 parts and builds the head's span-mean matrices; the Swift runtime is the reference
46
+ host. Images and video are not converted.
47
+
48
+ ## Quality
49
+
50
+ Reference: the same decoder in fp32 (matches Hugging Face's `Qwen3_5TextModel` to 5e-5 relative) streamed four
51
+ layers at a time, plus Cloudflare's own `JointSchemaHead`. 605 records / 611 questions (README samples, ARC-Easy and
52
+ ARC-Challenge test, first 300 each):
53
+
54
+ | | This model (8-bit Core ML) | fp32 reference | Cloudflare published |
55
+ |---|---:|---:|---:|
56
+ | answers that differ from fp32 | 1 / 611 (a near-tie: fp32 top-2 within 0.005) | — | — |
57
+ | median probability difference | 0.0004 | — | — |
58
+ | ARC-Easy | 100.0% | 100.0% | 99.5% |
59
+ | ARC-Challenge | 99.0% | 98.67% | 98.3% |
60
+
61
+ ARC rendering is Clef's record format with a plain state, not the Decision Index kit's templates, so compare within
62
+ about a point.
63
+
64
+ ## Speed (M5 Pro, 24 GB, GPU)
65
+
66
+ | | Median |
67
+ |---|---:|
68
+ | one decoder part (4 layers), 512-token bucket | 62 ms |
69
+ | **one record, 3 questions, ~370 tokens (L512)**, Swift runtime | **0.49 s** (p95 0.57 s) |
70
+ | load (compiled), 8 parts + head | ~40 s |
71
+
72
+ The decoder needs ~7 GB of memory to itself; with other large processes running it pages and slows down sharply.
73
+ The Qwen3.5 decoder (Gated DeltaNet layers) does not run on the Neural Engine: Core ML places 0 of its ops on the
74
+ ANE, so this is a GPU model.
75
+
76
+ ## Usage (Swift)
77
+
78
+ ```swift
79
+ import FluidUse
80
+
81
+ let manager = try await ClefFlashManager.load(from: bundleDirectory, bucket: 512)
82
+ let result = try await manager.answer(
83
+ state: "Our checkout is down and customers can't pay.",
84
+ questions: [
85
+ ("team", .choice(instructions: "Which team should handle this ticket?",
86
+ criteria: ["billing": "Charges, refunds", "engineering": "Bugs, outages"])),
87
+ ("urgency", .score(instructions: "How urgent is this ticket?", criteria: ["Low", "Normal", "High", "Critical"])),
88
+ ])
89
+ print(result.answers.map { ($0.questionID, $0.choice) }, result.totalMilliseconds)
90
+ ```
91
+
92
+ ## License and credits
93
+
94
+ Apache-2.0, same as [Cloudflare/clef-flash](https://huggingface.co/Cloudflare/clef-flash) (`LICENSE` is
95
+ Cloudflare's). All model weights are Cloudflare's; this repository only changes their format and precision.
96
+ Conversion by [Fluid Inference](https://fluidinference.com).
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+ "max_options": 96,
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+ "vision_end_token_id": 248054,
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