Huggingface to CoreML

This repository contains a script and information to convert a Huggingface models to CoreML.

Info

• Model to transform: microsoft/DialoGPT-small Try it by executing in command line $ python3 create-chat-model.py
• Also create this microsoft/DialoGPT-small

Links

• HuggingFace
• HuggingFace Transformers
• HuggingFace Core ML Models
• Using Stable Diffusion with Core ML on Apple Silicon
• Export Hugging Face models to Core ML and TensorFlow Lite
• Swift Core ML implementations of Transformers: GPT-2, DistilGPT-2, BERT, DistilBERT, more coming soon!
• Figuring out the shape of a Transformer Model To translate it to a coreML model
• Core ML Stable Diffusion
• Deploying Transformers on the Apple Neural Engine
• Swift app demonstrating Core ML Stable Diffusion
• Apple Core ML -BenderbotTokenizer doc
• BenderbotTokenizer implementation
• https://huggingface.co/microsoft/DialoGPT-small?text=Hi.
• Converting from PyTorch

Initial steps

0. Clone repo: $ git clone https://github.com/madcato/huggingface-coreml.git
1. Execute chatbot to try chatbot (optional), $ python3 try.py
2. Install huggingface exporters submodule, $ git submodule init && git submodule update
3. $ cd exporters && pip3 install -e . && cd ..
4. python3 export

The last step will create two mlpackage files in the exported folder: one for decoder and another for decoder part of the model. These files can be opened with Xcode and the model can be tested in the playground or project.

Try

• $ python3 -m exporters.coreml --model=t5-small --feature=text2text-generation exported
• $ python3 -m exporters.coreml --model=distilgpt2 --feature=text2text-generation exported

Exporters features

• 'feature-extraction',
• 'feature-extraction-with-past',
• 'fill-mask',
• 'image-classification',
• 'masked-im',
• 'multiple-choice',
• 'next-sentence-prediction',
• 'object-detection',
• 'question-answering',
• 'semantic-segmentation',
• 'text-classification',
• 'text-generation',
• 'text-generation-with-past',
• 'text2text-generation',
• 'token-classification',
• 'sequence-classification',
• 'causal-lm',
• 'causal-lm-with-past',
• 'seq2seq-lm',
• 'seq2seq-lm-with-past',
• 'speech2seq-lm',
• 'speech2seq-lm-with-past',
• 'masked-lm',
• 'vision2seq-lm',
• 'default',
• 'default-with-past',
• 'automatic-speech-recognition',
• 'ctc'