transformers.js
taichi.js
| transformers.js | taichi.js | |
|---|---|---|
| 27 | 3 | |
| 7,792 | 419 | |
| - | - | |
| 9.4 | 6.2 | |
| 6 days ago | about 1 year ago | |
| JavaScript | TypeScript | |
| Apache License 2.0 | - |
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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]
taichi.js
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Deep Learning in JavaScript
FWIW also taichi is quite popular in python and seems has some javascript related implementation (I haven't used it though), taichi.js [0]
[0] https://github.com/AmesingFlank/taichi.js
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How does Taichi differ from PyTorch? They are different in every sense!
The independent compilation and execution leaves Taichi with more possibilities. Though embedded in Python, Taichi is not Python-specific. In fact, our community developer AmesingFlank is working on a Javascript frontend. You can check out some cool preliminary demos in the taichi.js github repo.
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From molecular simulation to black hole rendering - Taichi-Lang makes life easier for digital content creators
Taichi.js is a powerful project that adds a JS frontend to Taichi and compiles Taichi to WASM with Emscripten, allowing users to transform Javascript functions into WebGPU compute shaders for massive parallelization. If your browser supports WebGPU, you can try it out on Playground | taichi.js.
What are some alternatives?
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
2d-fluid-simulator - 2D incompressible fluid solver implemented in Taichi.
web-stable-diffusion - Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
pyasflip - Python implementation of the ASFLIP advection method
web-ai - Run modern deep learning models in the browser.
examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
spark-nlp - State of the Art Natural Language Processing
taichi_elements - High-performance multi-material continuum physics engine in Taichi
memory64 - Memory with 64-bit indexes
taichimd - Interactive, GPU-accelerated Molecular Dynamics using the Taichi programming language
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
openai-java - OpenAI Api Client in Java