tokenizers-ruby
Fast state-of-the-art tokenizers for Ruby (by ankane)
models
A collection of pre-trained, state-of-the-art models in the ONNX format (by onnx)
| tokenizers-ruby | models | |
|---|---|---|
| 2 | 8 | |
| 117 | 7,249 | |
| - | 2.2% | |
| 7.5 | 4.8 | |
| 25 days ago | 15 days ago | |
| Rust | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tokenizers-ruby
Posts with mentions or reviews of tokenizers-ruby.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-11.
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Magnus 0.5 released (Library for writing Ruby gems in Rust)
tokenizers
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Text generation with GPT-2 in Ruby
The tokenizers gem is written in Rust, so installation requires Rust. If Rust isn't available, blingfire can be used instead.
models
Posts with mentions or reviews of models.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-05-06.
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Your 14-Day Free Trial Ain't Gonna Cut It
They're data-dependence graphs for a neural-network scheduling problem. Like this but way bigger to start with and then lowered to more detailed representations several times: https://netron.app/?url=https://github.com/onnx/models/raw/m... My home-grown layout engine can handle the 12k nodes for llama2 in its highest-level form in 20s or so, but its not the most featureful, and they only get bigger from there. So I always have an eye out for potential tools.
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AMD Accelerates AI Adoption on Windows 11 With New Developer Tools for Ryzen AI
Uh, maybe they didn't feel the need to look. I already pointed you to the ONNX project. Here are some ONNX-based. These are just the ones being shared with the community. The limit of AMD's responsibility is writing the low-level libraries to support ONNX.
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Need Help With Darknet YOLOv4-Tiny Model In Unity Barracuda
I am new to object detection models and I need help running my object detection Darknet YOLOv4-Tiny Model In Unity Barracuda. I trained my model and then i converted it to ONNX format with 2 methods. One method was using pytorch-YOLOv4 from github and the other by converting my model to tensorflow and then to onnx and shown here: "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb"
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Need Help Converting Darknet Yolov4-tiny Model to ONNX
Then i tried to convert it again using another method that i found here "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb" in order to convert it from darknet to tensorflow and then to onnx but i didn't have any luck.
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Text generation with GPT-2 in Ruby
Here we use the GPT-2 model distributed by the ONNX official. Download GPT-2-LM-HEAD from the link.
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YOLOv7 object detection in Ruby in 10 minutes
Download pre-trained models from the ONNX Model Zoo
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Has anyone successfully converted an onnx model to tensorflow? Here's the problems I'm having...
Instructions to reproduce the problem: I am trying to convert a proprietary model at work but for now i'll use mobilenetv2-7.onnx to explain/reproduce the issue.
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How to identify identical frames that are not technically duplicates? Ie if I am taking a video of a car, it stops for 1 minute (and within that minute nothing changes visually), and then drives away. How would I remove all but 1 of the frames when it is stopped?
One approach could be run a pre-trained object detector (like one of these) on each frame and then a simple object tracker on top of it (like this).
What are some alternatives?
When comparing tokenizers-ruby and models you can also consider the following projects:
magnus - Ruby bindings for Rust. Write Ruby extension gems in Rust, or call Ruby from Rust.
SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2 - This repository contains the script and process to create custom SSD Mobilenet model for object detection
halton-rb - A Ruby library, written in Rust, for generating Halton sequences
netron - Visualizer for neural network, deep learning and machine learning models
yrb - Ruby bindings for yrs.
onnx-tensorflow - Tensorflow Backend for ONNX
rucaptcha - Captcha Gem for Rails, which generates captcha image by Rust.
redisai-examples - RedisAI showcase
selma - Selma selects and matches HTML nodes using CSS rules. Backed by Rust's lol_html parser.
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
wasmtime-rb - Ruby WebAssembly runtime powered by Wasmtime
tensorboard - TensorFlow's Visualization Toolkit
tokenizers-ruby vs magnus
models vs SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2
tokenizers-ruby vs halton-rb
models vs netron
tokenizers-ruby vs yrb
models vs onnx-tensorflow
tokenizers-ruby vs rucaptcha
models vs redisai-examples
tokenizers-ruby vs selma
models vs TensorFlow-Examples
tokenizers-ruby vs wasmtime-rb
models vs tensorboard