| commit | d12291f7fb615200a2a98c8f9ca45af5dde52684 | [log] [tgz] |
|---|---|---|
| author | Scott Todd <scott.todd0@gmail.com> | Mon Apr 22 14:40:50 2024 -0700 |
| committer | GitHub <noreply@github.com> | Mon Apr 22 14:40:50 2024 -0700 |
| tree | 15f6e9680e0ef56babcc65546dd41d93bb667865 | |
| parent | 36b3ce1fc5c534339643cdda6ace759fda0e0fa6 [diff] |
Add Colab notebook showing Hugging Face import via Turbine. (#17093) This shows a minimal import from Hugging Face using the style of code from https://github.com/nod-ai/SHARK-TestSuite/tree/main/e2eshark/pytorch/models. I'd like to expand on this with other advanced export APIs (e.g. using parameter files like Safetensors/GGUF/IRPA, Turbine-LLM, and custom kernels), but figured I'd start with something fairly well tested and simple. The test case I forked from also used a text prompt for an audio -> text model, so this just shows some numbers as output... I'd like to show text or images as output, though that needs some interfacing (WhisperProcessor wraps WhisperFeatureExtractor and WhisperTokenizer, for example).
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.