onnxruntime
transformers.js
onnxruntime | transformers.js | |
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54 | 26 | |
12,736 | 7,587 | |
2.7% | - | |
10.0 | 9.4 | |
7 days ago | 5 days ago | |
C++ | JavaScript | |
MIT License | Apache License 2.0 |
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onnxruntime
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Machine Learning with PHP
ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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AI Inference now available in Supabase Edge Functions
Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.
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Deep Learning in JavaScript
tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.
- FLaNK Stack 05 Feb 2024
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Vcc – The Vulkan Clang Compiler
- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.
The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.
[1]: https://www.circle-lang.org/
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Oracle-samples/sd4j: Stable Diffusion pipeline in Java using ONNX Runtime
I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.
For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.
- Mamba-Chat: A Chat LLM based on State Space Models
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VectorDB: Vector Database Built by Kagi Search
What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.
If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).
- ONNX runtime: Cross-platform accelerated machine learning
- Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
transformers.js
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Transformers.js: Machine Learning for the Web
We have some other WebGPU demos, including:
- WebGPU embedding benchmark: https://huggingface.co/spaces/Xenova/webgpu-embedding-benchm...
- Real-time object detection: https://huggingface.co/spaces/Xenova/webgpu-video-object-det...
- Real-time background removal: https://huggingface.co/spaces/Xenova/webgpu-video-background...
- WebGPU depth estimation: https://huggingface.co/spaces/Xenova/webgpu-depth-anything
- Image background removal: https://huggingface.co/spaces/Xenova/remove-background-webgp...
You can follow the progress for full WebGPU support in the v3 development branch (https://github.com/xenova/transformers.js/pull/545).
To answer your question, while there are certain ops missing, the main limitation at the moment is for models with decoders... which are not very fast (yet) due to inefficient buffer reuse and many redundant copies between CPU and GPU. We're working closely with the ORT team to fix these issues though!
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Deep Learning in JavaScript
BTW: you might want to add support for typed arrays.
See: https://github.com/xenova/transformers.js/blob/8804c36591d11...
This is really old, but added as part of the shape of the vector as well: https://github.com/nicolaspanel/numjs/blob/master/src/dtypes...
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Deja-Vu your AI✦ Bookmarking Tool
Made possible by Xenova and Supabase / gte-small
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Extracting YouTube video data with OpenAI and LangChain
To build the application, you’ll use the youtube-transcript package to retrieve YouTube video transcripts. You will then use LangChain and the Transformers.js package to generate free Hugging Face embeddings for the given transcript and store them in a vector store instead of relying on potentially expensive OpenAI embeddings. Lastly, you will use LangChain and an OpenAI model to retrieve information stored in the vector store.
- Transformers.js releases Zero-shot audio classification support
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How to Use AI/ML Models for Your Projects
Transformers.js: A groundbreaking library, Transformers.js brings transformer models like GPT-3, BERT, and Whisper straight to your browser. With the introduction of technologies like webGPU and LLM, Transformers.js has garnered significant attention. If you’d like to learn how to integrate a small model in the UI, check out their code and examples here.
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Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks
The announcement seems somewhat disingenuous. The PR[1] found from their release notes[2] seems to contain only boilerplate and no real support for Mistral models or their weights.
[1]: https://github.com/xenova/transformers.js/pull/379
- Transformers.js
- Transformers.js: Run Machine Learning models directly in the browser
- What is the most cost-efficient way to have an embedding generator endpoint that is using an open-source embedding model? [Q]
What are some alternatives?
onnx - Open standard for machine learning interoperability
web-stable-diffusion - Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
web-ai - Run modern deep learning models in the browser.
onnx-simplifier - Simplify your onnx model
spark-nlp - State of the Art Natural Language Processing
ONNX-YOLOv7-Object-Detection - Python scripts performing object detection using the YOLOv7 model in ONNX.
memory64 - Memory with 64-bit indexes
onnx-tensorflow - Tensorflow Backend for ONNX
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
MLflow - Open source platform for the machine learning lifecycle
openai-java - OpenAI Api Client in Java