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clef-text-0.6b-coreml

FluidInference / clef-text-0.6b-coreml

Run on huggingface.co
huggingface.co
Total runs: 194
24-hour runs: 10
7-day runs: 179
30-day runs: 179
Model's Last Updated: October 11 2026
text-classification

Introduction of clef-text-0.6b-coreml

Model Details of clef-text-0.6b-coreml

clef-text-0.6b (Core ML)

A 0.70B-parameter text decision model for Apple silicon, distilled from Cloudflare's clef-flash (9B) into Qwen3-0.6B plus Clef's joint schema head. Same contract as Clef and the Jev / SystemOne API: a state and typed questions ( noul , choice , score ) in, a probability for every option of every question out, one forward pass.

Runs on the GPU or 100% on the Neural Engine (every decoder op placed on the ANE at all four buckets). PyTorch weights: FluidInference/clef-text-0.6b . Swift runtime: ClefTextManager in FluidUse .

Packages
File Role Size
Decoder.mlpackage Qwen3-0.6B decoder + final norm, fp16; functions L256 L512 L1024 L2048 (shared weights) 848 MB
Head.mlpackage joint schema head, fp16, ≤ 16 questions / ≤ 96 options, same four functions 197 MB
embeddings.f16 token embeddings (tied: also the head's lexical option rows) 297 MB
tokenizer.json , config.json tokenizer; shapes and ids for the host

Decoder inputs: hidden [1, L, 1024] , cos / sin [L, 128] , additive causal mask [1, 1, L, L] (all fp16) → states [1, L, 1024] .

Quality

Held-out test splits (official test / validation sets, 5,124 questions), gold accuracy:

Task this model clef-flash 9B (teacher)
DBpedia-14 entity type 99.7 100.0
AG News topic 90.7 92.0
SST-2 sentiment 90.0 94.0
BANKING77 intent (77-way) 86.4 94.5
TweetEval offensive 85.0 82.3
Yelp stars (5-way) 69.0 70.7
Emotion (6-way) 68.0 58.0
BoolQ 82.3 90.7
MNLI 72.3 84.3
ARC-Easy 80.0 100.0
CommonsenseQA 65.0 94.3
ARC-Challenge 60.7 97.3
all 79.1 88.2

Good at routing and classification (within 0–4 points of the teacher on topic, entity, sentiment, rating, toxicity, emotion); not a knowledge model — science / commonsense QA and NLI stay well below the 9B. Teacher agreement 86.4%, calibration ECE 0.046 (dev).

Core ML on the Neural Engine vs the PyTorch student on the same 5,118 test questions: 15 different answers (0.3%, all near-ties: PyTorch top-2 within 0.11), median probability drift 0.013, gold accuracy equal within ±0.7 per task.

Speed (M5 Pro)

Decoder at 512 tokens: 35 ms on the GPU ( .cpuAndGPU ), 64 ms on the Neural Engine ( .cpuAndNeuralEngine ), 200 ms CPU-only. On this Mac the GPU is ~2× faster; the Neural Engine leaves the GPU free for other work.

Bucket Neural Engine, decoder + head
256 tokens 29 ms
512 tokens 69 ms
1,024 tokens 196 ms
2,048 tokens 591 ms

Swift runtime, 3-question support ticket ( 370 tokens): ** 48 ms on the GPU** (1,000 tickets in 57 s in the FluidUse demo), ~77 ms on the Neural Engine — vs ~0.5 s for clef-flash Core ML (9B, GPU).

Training

24,208 records from 12 public datasets (train splits; test splits held out) labelled by clef-flash (8-bit Core ML, 1 flip / 611 vs fp32), KL (T = 2) + 0.3 gold cross-entropy, LoRA r64 on the backbone + full head, 2 epochs. The head starts from clef-flash's head weights wherever the shapes match.

License and credits

Apache-2.0. Teacher: Cloudflare/clef-flash (Apache-2.0); base: Qwen/Qwen3-0.6B (Apache-2.0). By Fluid Inference .

Runs of FluidInference clef-text-0.6b-coreml on huggingface.co

194
Total runs
10
24-hour runs
48
3-day runs
179
7-day runs
179
30-day runs

More Information About clef-text-0.6b-coreml huggingface.co Model

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clef-text-0.6b-coreml install

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Url of clef-text-0.6b-coreml

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