Examples developed using GoMLX

Examples Trained from Scratch

The UCI-Adult/Census (Notebook) is the canonical example, that you can copy from when creating a new model from scratch. See the full code (including the Jupyter NotebooK) in gomlx/examples/adult.

Other examples:

• UCI-Adult/Census model;
• How do KANs learn ?;
• Cifar-10 demo;
• MNIST demo (library and command-line only)
• Dogs & Cats classifier demo;
• IMDB Movie Review demo;
• Diffusion model for Oxford Flowers 102 dataset (generates random flowers);
  • Flow Matching Study Notebook based on Meta’s “Flow Matching Guide and Code”.
• GNN model for OGBN-MAG (experimental).
• Last, a trivial synthetic linear model, for those curious to see a barebones simple model.
• Neural Style Transfer 10-year Celebration: see a demo written using GoMLX of the original paper.
• Triplet Losses: various negative sampling strategies as well as various distance metrics.
• AlphaZero AI for the game of Hive: it uses a trivial GNN to evaluate positions on the board. It includes a WASM demo (runs GoMLX in the browser!) and a command-line UI to test your skills!

Imported Models Examples

Imported models from ONNX or HuggingFace can be further fine-tuned, transfer-learned, composed, etc, using GoMLX.

• 🚀 NEW 🚀 SAM2: Segment Anything Model (Facebook): model to segment images (videos version not ported yet).
• 🚀 NEW 🚀 Gemma4-4B-it library and demo: Google’s new free generative LLM, instrunction tuned. See also HuggingFace’s “google/gemma-4-E4B-it” model page.
• KaLM-Gema3 12B parameters: Tecent’s top-ranked sentence encoder for RAGs, using go-huggingface to load the model and tokenizer, and GoMLX to execute it.
• Gemma 3 270M: Demonstrates ONNX-converted text generation (LLM) using the onnx-community/gemma-3-270m-it-ONNX model with GoMLX. It uses the gomlx/onnx-gomlx package to convert the model, and gomlx/go-huggingface to download the model and run the * 🚀 NEW 🚀 GPT-2: Demonstrates text generation using the the new (experimental) transformer and generator packages. tokenizer.
• BERT-base-NER: A BERT-base model fine-tuned for Named Entity Recognition. It’s also a ONNX-converted model from dslim/bert-base-NER model from HuggingFace.
• MixedBread Reranker v1: A cross-encoder reranking example, see HuggingFace MixedBread Reranker v1 page. It uses the gomlx/onnx-gomlx package to convert the model, and gomlx/go-huggingface to download the model and run the tokenizer.