smolrsrwkv
A relatively basic implementation of RWKV in Rust written by someone with very little math and ML knowledge. Supports 32, 8 and 4 bit evaluation. It can also directly load PyTorch RWKV models. (by KerfuffleV2)
tract
Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference (by sonos)
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smolrsrwkv | tract | |
---|---|---|
6 | 20 | |
90 | 2,046 | |
- | 2.7% | |
5.6 | 9.8 | |
8 months ago | 6 days ago | |
Rust | Rust | |
MIT License | Apache 2.0/MIT |
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.
smolrsrwkv
Posts with mentions or reviews of smolrsrwkv.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-06.
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Introducing repugnant-pickle, a crate for scraping Python Pickle files in a basic way. Notable, it can deal with (some) PyTorch model files.
For an example of actually using it, you can look at my little project for running inference on RWKV models: https://github.com/KerfuffleV2/smolrsrwkv It uses repugnant-pickle to enable loading PyTorch models directly with no conversion requirement or Python dependencies.
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Is GPT-4 still just a language model trying to predict text?
If you want some proof, I wrote my own application that can run inference on RWKV models (competing approach similar to GPT which most LLMs use currently): https://github.com/KerfuffleV2/smolrsrwkv
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I created a simple implementation of the RWKV language model (RWKV competes with the dominant Transformers-based approach which is the "T" in GPT)
It can now quantize to 8bit for 4x memory savings: https://github.com/KerfuffleV2/smolrsrwkv/tree/experiment-quantize
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ChatGPT saved this dog's life...
Here's an implementation of RWKV I wrote that can run inference on models: https://github.com/KerfuffleV2/smolrsrwkv
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LLMs are not that different from us -- A delve into our own conscious process
Now, I'm not an expert but I do know a little more than the average person. I actually just got done implementing a simple one based on the RWKV approach rather than transformers: https://github.com/KerfuffleV2/smolrsrwkv
tract
Posts with mentions or reviews of tract.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-03.
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Are there any ML crates that would compile to WASM?
Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.
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[Discussion] What crates would you like to see?
tract!!
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tract VS burn - a user suggested alternative
2 projects | 25 Mar 2023
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Machine Learning Inference Server in Rust?
we use tract for inference, integrated into our runtime and services.
- onnxruntime
- Rust Native ML Frameworks?
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Neural networks - what crates to use?
Not for training, but for inference this looks nice: https://github.com/sonos/tract
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Brain.js: GPU Accelerated Neural Networks in JavaScript
There's also tract, from sonos[0]. 100% rust.
I'm currently trying to use it to do speech recognition with a variant of the Conformer architecture (exported to ONNX).
The final goal is to do it in WASM client-side.
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Serving ML at the Speed of Rust
As the article notes, there isn't any official Rust-native support for any common frameworks.
tract (https://github.com/sonos/tract) seems like the most mature for ONNX (for which TF/PT export is good nowadays), and recently it successfully implemented BERT.
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Run deep neural network models from scratch
There are some DL libraries written in Rust: https://github.com/sonos/tract , https://docs.rs/neuronika/latest/neuronika/index.html . The second one could be used for training, I think.