rust-bert
tokenizers
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rust-bert | tokenizers | |
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7 | 8 | |
2,418 | 8,424 | |
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about 2 months ago | about 2 hours ago | |
Rust | Rust | |
Apache License 2.0 | Apache License 2.0 |
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rust-bert
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How to leverage the state-of-the-art NLP models in Rust
brew install libtorch brew link libtorch brew ls --verbose libtorch | grep dylib export LIBTORCH=$(brew --cellar pytorch)/$(brew info --json pytorch | jq -r '.[0].installed[0].version') export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH git clone https://github.com/guillaume-be/rust-bert.git cd rust-bert ORT_STRATEGY=system cargo run --example sentence_embeddings
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Transformers.js
I'd like to use this transformer model in rust (because it's on the backend, because I can use data munging and it will be faster, and for other reasons). It looks like a good model! But, it doesn't compile on Apple Silicon for wierd linking issues that aren't apparent - https://github.com/guillaume-be/rust-bert/issues/338. I've spent a large part of today and yesterday attempting to find out why. The only other library that I've found for doing this kind of thing programmatically (particularly sentiment analysis) is this (https://github.com/JohnSnowLabs/spark-nlp). Some of the models look a little older, which is OK, but it does mean that I'd have to do this in another language.
Does anyone know of any sentiment analysis software that can be tuned (other than VADER - I'm looking for more along the lines of a transformer model) - like BERT, but is pretrained and can be used in Rust or Python? Otherwise I'll probably using spark-nlp and having to spin another process.
Thanks.
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Running large language models like ChatGPT on a single GPU
Give this a look: https://github.com/guillaume-be/rust-bert
If you have Pytorch configured correctly, this should "just work" for a lot of the smaller models. It won't be a 1:1 ChatGPT replacement, but you can build some pretty cool stuff with it.
> it's basically Python or bust in this space
More or less, but that doesn't have to be a bad thing. If you're on Apple Silicon, you have plenty of performance headroom to deploy Python code for this. I've gotten this library to work on systems with as little as 2gb of memory, so outside of ultra-low-end use cases, you should be fine.
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Self-hosted Whisper-based voice recognition server for open Android phones
I suspect something similar is possible with ChatGPT. Using the GPT-neo-125m model I've been able to get some really convincing (if lackluster) answers on 4 core ARM hardware and less than 2gb of memory. With enough sampling, you can get legible paragraph-length responses out in less than 10 seconds; that's pretty good for an offline program in my book.
I'm using rust-bert to serve it over a Discord bot, similar to one of their examples[0]. It's running on Oracle VCPUs right now, but with dedi hardware and ML acceleration I can imagine the field moving really quickly.
[0] https://github.com/guillaume-be/rust-bert/blob/master/exampl...
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Ask HN: What AI developer tools do you wish you'd discovered sooner?
Maybe a little played-out, but I've been having a blast with the rust-bert library this weekend: https://github.com/guillaume-be/rust-bert
With a little fanagling, you can get the GPT-Neo-1.3b model running on those free Oracle ARM VMs you can provision. I'm impressed, especially with the performance of the smallest model that uses less than a gig of memory.
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Ask HN: Has anyone made a toy that integrates ChatGPT with voice into a toy?
Nope, but it's probably possible on a smaller, hobbyist scale. I've been playing with a few GPT libraries this week (namely rust-bert[0]) and I've been really impressive with local generation results on my crappy 2 core netbook. I can get 2 sentences to generate in ~5 seconds, which is pretty good in my book.
Armed with a Pi-style SBC and your AI library of choice, I bet you could get pretty far implementing some stuff. Bonus points if you use Whisper for speech-to-text, and double brownie points if you can get an AI voice to read the generation back.
[0] https://github.com/guillaume-be/rust-bert/tree/master/exampl...
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[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?
If you are using BERT models and some miscellaneous other related stuff then you should check out the rust-bert and Bert Sentence repos https://github.com/guillaume-be/rust-bert
tokenizers
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HF Transfer: Speed up file transfers
Hugging Face seems to like Rust. They also wrote Tokenizers in Rust.
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LLM custom dictionary
Your intuition is right. There are two ways (in increasing order of result performance) : 1. You can simply extend vocab file of the tokenizer and test the predictions 2. You can extend the vocab file and re-train your model on custom data which has these new tokens. Check the following issue on GitHub : https://github.com/huggingface/tokenizers/issues/247
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[D] SentencePiece, WordPiece, BPE... Which tokenizer is the best one?
SentencePiece -> implementation of some algorithms (there are several others, https://github.com/microsoft/BlingFire https://github.com/glample/fastBPE https://github.com/huggingface/tokenizers )
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Portability of Rust in 2021
In sum I would like the idea to go with Rust as I more or less got to rewrite the whole thing anyway, but I am a bit skeptical if I will be able to interface with everything that might come up at some point. Or probably end up in a wrapper hell if I got to use more C++ libraries. On the other hand there are definitely a few Rust projects out there that might come in handy (for example https://github.com/huggingface/tokenizers). And the build process is pretty awful right now (CMake it is but with lots of hacks).
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[D] What's going to be the dominant language for machine learning in 5 years?
A full machine learning pipeline usually comprises far more than just the model, and this is the area where Rust may shine (the recent work by HuggingFace and their https://github.com/huggingface/tokenizers library is a good example)
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substitute for tokenizer in torchtext
As for other tokenizers, you can take a look at - Huggingface tokenizers library: https://github.com/huggingface/tokenizers - NLTK tokenize: https://www.nltk.org/api/nltk.tokenize.html - Polygot: https://pypi.org/project/polyglot/
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PyO3: Rust Bindings for the Python Interpreter
Huggingface Tokenizers (https://github.com/huggingface/tokenizers), which are now used by default in their Transformers Python library, use pyO3 and became popular due to the pitch that it encoded text an order of magnitude faster with zero config changes.
It lives up to that claim. (I had issues with return object typing when going between Python/Rust at first but those are more consistent now)
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Rusticles #19 - Wed Nov 11 2020
huggingface/tokenizers (Rust): 💥Fast State-of-the-Art Tokenizers optimized for Research and Production
What are some alternatives?
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
onnx-tensorflow - Tensorflow Backend for ONNX
speak - Talk with your machine in this minimalistic Rust crate!
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
setuptools-rust - Setuptools plugin for Rust support
are-we-learning-yet - How ready is Rust for Machine Learning?
BlingFire - A lightning fast Finite State machine and REgular expression manipulation library.
ggml - Tensor library for machine learning
rayon - Rayon: A data parallelism library for Rust
lightseq - LightSeq: A High Performance Library for Sequence Processing and Generation
tch-rs - Rust bindings for the C++ api of PyTorch.