Instructions to use iky1e/punctuation-fullstop-truecase-english-mlx-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use iky1e/punctuation-fullstop-truecase-english-mlx-q8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir punctuation-fullstop-truecase-english-mlx-q8 iky1e/punctuation-fullstop-truecase-english-mlx-q8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
English punctuation, capitalization, and segmentation MLX — Q8
This repository contains 8-bit affine weight quantization with group size 64 and FP16 residual tensors for Apple-silicon inference with MLX.
It is converted from 1-800-BAD-CODE/punctuation_fullstop_truecase_english at revision b26fd1c40e88678859048898218ea4edcc24c84a. The original model restores punctuation and capitalization and predicts sentence boundaries for lowercase English text. Its source ONNX SHA-256 is dd922d459da618cd324280889740608b76fb3e9e61d3f402291be1251f91421b.
MLX variant comparison
| Variant | Repository | Weight file | Size vs ONNX FP32 | Character agreement | Word agreement | Formatter inference |
|---|---|---|---|---|---|---|
| ONNX FP32 source | 1-800-BAD-CODE/punctuation_fullstop_truecase_english | 209.5 MB | 100.0% | 100.0000% | 100.0000% | 2.044s |
| FP16 | iky1e/punctuation-fullstop-truecase-english-mlx-fp16 | 104.7 MB | 50.0% | 99.9916% | 99.9559% | 0.417s |
| Q8 (this repository) | iky1e/punctuation-fullstop-truecase-english-mlx-q8 | 55.8 MB | 26.6% | 99.9536% | 99.7575% | 0.260s |
| Q6 | iky1e/punctuation-fullstop-truecase-english-mlx-q6 | 42.8 MB | 20.4% | 99.8777% | 99.3681% | 0.346s |
| Q5 | iky1e/punctuation-fullstop-truecase-english-mlx-q5 | 36.2 MB | 17.3% | 99.6066% | 97.9941% | 0.350s |
| Q4 | iky1e/punctuation-fullstop-truecase-english-mlx-q4 | 29.7 MB | 14.2% | 99.2899% | 96.4879% | 0.330s |
Agreement is measured against the original ONNX FP32 model's formatted output, not against a human transcript. Character and word agreement are 100 − normalized Levenshtein distance. The input was Granite Q8's raw transcript of a 6,118.72-second (101m58.72s) Stanford CME295 lecture: 69,168 input characters and 71,116 ONNX-formatted characters. Formatter time excludes process startup and model loading. Peak RSS from the Python harness includes Python and framework overhead.
Q8 is the recommended default: it is substantially smaller than FP16 while remaining very close to ONNX FP32 and was the fastest measured MLX variant on this machine.
Granite-MLX usage
Formatted output is intended to be the Granite-MLX default:
granite-mlx recording.mp4 --punctuation-model iky1e/punctuation-fullstop-truecase-english-mlx-q8
Use --no-punctuate when exact raw Granite CTC text or minimum memory usage is required.
Files
model.safetensors: MLX weightsmlx_config.json: architecture, precision, quantization, and source metadatatokenizer.jsonandtokenizer_config.json: native-compatible SentencePiece Unigram tokenizerspe_32k_lc_en.model: original SentencePiece model for parity/reference runtimesconfig.yaml: source labels and sequence configuration
Reproducing conversion
uv run python Scripts/convert_punctuation.py \
/path/to/punctuation_fullstop_truecase_english \
/path/to/output-q8 \
--bits 8
The converter and complete benchmark artifacts are maintained in the Granite-MLX project.
License
These converted model weights retain the original model's Apache 2.0 license. Granite-MLX is separate software that downloads and runs a user-selected checkpoint.
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