mobius

ONNX model definitions for GenAI using the onnxscript.nn API.

Overview

This package provides model definitions for generative AI architectures — LLMs, MoE, multimodal, encoder-only, encoder-decoder, vision, audio, and diffusion models — built directly as ONNX graphs using onnxscript.nn.Module. Rather than tracing or exporting PyTorch models, it constructs the ONNX graph declaratively, then applies pretrained HuggingFace weights.

Supports building ONNX models from HuggingFace model IDs with automatic weight downloading, dtype casting (including bfloat16 via ir.LazyTensor), and multi-component export for pipelines.

📖 Documentation · 📦 Supported Models

Highlighted Models

Category	Examples
Text Generation	Llama 2/3/4, Mistral, Qwen 2/2.5/3/3.5/3.6, Phi-3/3.5, Gemma 1/2/3/4, Granite, GPT-2, OPT, OLMo, SmolLM3, and many more
Mixture of Experts	PhiMoE, GPTOSS, Mixtral, OLMoE, DeepSeek-V2/V3, Qwen2-MoE, Qwen3-MoE, Qwen3-Next, GLM-4-MoE, Arctic, DBRX, Jamba
Multimodal	Gemma 3/4, Phi-4MM (vision + audio + LoRA), LLaVA, InternVL2, Qwen2.5-VL, Qwen3-VL, Qwen3.5/3.6-VL, Pixtral
Encoder-only	BERT, RoBERTa, ALBERT, DeBERTa, DistilBERT, ELECTRA, XLNet
Encoder-Decoder	BART, T5/mT5, Marian, M2M-100, Pegasus, BigBird-Pegasus
Speech-to-Text	Whisper, FastConformer-RNNT, FunASR, Qwen3-ASR, SenseVoice
Audio	Wav2Vec2, HuBERT, WavLM, SpeechT5
Vision	ViT, BEiT, DeiT, DINOv2, Swin, CLIP, SigLIP
Diffusion	Stable Diffusion (UNet + VAE + ControlNet), Flux, SD3, DiT, QwenImage, HunyuanDiT, CogVideoX
Adapters	T2I-Adapter, IP-Adapter

Supports 290+ Transformers model types and 10 Diffusers component types across 40+ task types and 100+ reusable components.

See the model documentation for the complete list.

Installation

pip install -e .

For running tests:

pip install -e ".[testing]"

Quick Start

Python API

from mobius import build

# Build a model package with weights
pkg = build("meta-llama/Llama-3.2-1B")
pkg.save("output/llama-3.2-1b/")

Static cache (opt-in) pre-allocates fixed-size KV cache buffers, which is useful when you know the maximum sequence length up front:

from mobius import build, CausalLMTask

task = CausalLMTask(static_cache=True, max_seq_len=2048)
pkg = build("meta-llama/Llama-3.2-1B", task=task)
pkg.save("output/llama-3.2-1b-static/")

EP-aware optimization generates graphs tuned for a specific runtime execution provider. Pass execution_provider to target CUDA, DirectML, WebGPU, and more — each with the right set of fused kernels and lowering passes applied automatically:

from mobius import build

# CUDA: GQA fusion, SkipLayerNorm, PackQKV
pkg = build("meta-llama/Llama-3.2-1B",
            execution_provider="cuda", dtype="f16")

# WebGPU: GQA fusion, Shape ops replaced with portable alternatives
pkg = build("meta-llama/Llama-3.2-1B",
            execution_provider="webgpu", dtype="f16")

See the EP quickstart and full EP reference for all supported EPs and options.

CLI

mobius build --model Qwen/Qwen2.5-0.5B output_dir/

# Build for CUDA with f16
mobius build --model meta-llama/Llama-3.2-1B output_dir/ --ep cuda --dtype f16

# Build a diffusers pipeline (all components)
mobius build --model Qwen/Qwen-Image-2512 output_dir/

# Build encoder-decoder model (produces encoder/model.onnx + decoder/model.onnx)
mobius build --model openai/whisper-tiny output_dir/

See the CLI Reference for all subcommands and flags.

Examples

• examples/build_and_save.py — Build and save ONNX models (simplest workflow)
• examples/text_generation.py — Greedy text generation with a causal LM
• examples/static_cache_generation.py — Text generation with static KV cache
• examples/multimodal_generation.py — Image captioning with a multimodal model

Architecture

HuggingFace Hub
       │
       ▼
 ArchitectureConfig ◄── from_transformers() / from_diffusers()
       │
       ▼
 Model Module ◄── Reusable Components (Attention, MLP, RMSNorm, RoPE, …)
       │
       ▼
 Task ◄── CausalLMTask, VisionLanguageTask, VAETask, DenoisingTask, …
       │
       ▼
 ONNX Model ◄── preprocess_weights() + apply_weights()

The package is organised into four layers:

• Components — onnxscript.nn.Module building blocks (Attention, MLP, DecoderLayer, RoPE, VisionEncoder, MoELayer, …)
• Models — Full architectures composed from components
• Tasks — Define the ONNX graph I/O contract (inputs, outputs, KV cache)
• Registry — Maps HuggingFace model_type strings to model classes

See the design document for details.

Development

# Unit tests (fast, no network needed)
pytest tests/build_graph_test.py -v

# Integration tests (downloads models)
pytest tests/integration_test.py -m integration -v

# All unit tests (components, configs, tasks, models)
pytest src tests -m "not integration" -v

# Linting
lintrunner f --all-files

Adding a new model

See the AI-assisted model support strategy and the developer skills in .agents/skills/:

Skill	Use when
adding-a-new-model	Adding any new HuggingFace model architecture
reusable-components	Creating or extending components
moe-models	Adding a Mixture-of-Experts model
multimodal-models	Adding a vision-language model
writing-tests	Writing unit or integration tests
writing-rewrite-rules	Adding ONNX graph rewrite rules