candle
burn
candle | burn | |
---|---|---|
17 | 34 | |
13,475 | 4,845 | |
4.4% | - | |
9.9 | 8.9 | |
4 days ago | 5 months ago | |
Rust | Rust | |
Apache License 2.0 | Apache License 2.0 |
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.
candle
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karpathy/llm.c
Candle already exists[1], and it runs pretty well. Can use both CUDA and Metal backends (or just plain-old CPU).
[1] https://github.com/huggingface/candle
- Best alternative for python
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Is there any LLM that can be installed with out python
Check out Candle! It's a Deep Learning framework for Rust. You can run LLMs in binaries.
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Announcing Kalosm - an local first AI meta-framework for Rust
Kalosm is a meta-framework for AI written in Rust using candle. Kalosm supports local quantized large language models like Llama, Mistral, Phi-1.5, and Zephyr. It also supports other quantized models like Wuerstchen, Segment Anything, and Whisper. In addition to local models, Kalosm supports remote models like GPT-4 and ada embeddings.
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RFC: candle-lora
I have been working on a machine learning library called candle-lora for Candle. It implementes a technique called LoRA (low rank adaptation), which allows you to reduce a model's trainable parameter count by wrapping and freezing old layers.
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ExecuTorch: Enabling On-Device interference for embedded devices
[2] https://github.com/huggingface/candle/issues/313
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[P] Open-source project to run locally LLMs in browser, such as Phi-1.5 for fully private inference
We provide full local inference in browser, by using libraries from Hugging Face like transformers.js or candle for WASM inference.
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Update on the Candle ML framework.
We've first announced Candle, a minimalist ML framework in Rust 6 weeks ago. Since then we've focused on adding various recent models and improved the framework so as to support the necessary features in an efficient way. You can checkout a gallery of the examples, supported models include:
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Should I Haskell or OCaml?
How did you select those two as your options?
I'm just a hobbyist that enjoys programming, and I eventually wanted to expand beyond python. I looked at Haskell and read Learn You a Haskell and did some Exercism exercises but never got anywhere close to being able to use it for real projects. Have been trying to learn about Lisp lately and feel like I've come to a similar dead end.
On the other hand, both Go and Rust have felt fulfilling and practical, with static typing and solid tooling, cross compilations, static binaries, and dependency management that is just a huge breath of fresh air coming from python.
The ML / data science scene is nowhere near as developed as in Python, and I still lean on jupyter/polars/PyTorch here, but I think the candle project[0] seems very interesting. Compiling whisper down to a single CUDA-leveraging binary for fast local transcription is pretty cool!
[0]: https://github.com/huggingface/candle
- Minimalist ML framework for Rust
burn
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Burn 0.10.0 Released 🔥 (Deep Learning Framework)
Release Note: https://github.com/burn-rs/burn/releases/tag/v0.10.0
- Deep Learning Framework in Rust: Burn 0.10.0 Released
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Why Rust Is the Optimal Choice for Deep Learning, and How to Start Your Journey with the Burn Deep Learning Framework
The comprehensive, open-source deep learning framework in Rust, Burn, has recently undergone significant advancements in its latest release, highlighted by the addition of The Burn Book 🔥. There has never been a better moment to embark on your deep learning journey with Rust, as this book will guide you through your initial project, providing extensive explanations and links to relevant resources.
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Candle: Torch Replacement in Rust
Burn (deep learning framework in rust) has WGPU backend (WebGPU) already. Check it out https://github.com/burn-rs/burn. It was released recently.
- Burn – A Flexible and Comprehensive Deep Learning Framework in Rust
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Announcing Burn-Wgpu: New Deep Learning Cross-Platform GPU Backend
For more details about the latest release see the release notes: https://github.com/burn-rs/burn/releases/tag/v0.8.0.
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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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Any working wgpu compute example that would run in a browser?
We, the burn team, are working on the wgpu backend (WebGPU) for Burn deep learning framework. You can check out the current state: https://github.com/burn-rs/burn/tree/main/burn-wgpu
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I’ve fallen in love with rust so now what?
Here is the project: https://github.com/burn-rs/burn
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Is anyone doing Machine Learning in Rust?
Disclaimer, I'm the main author of Burn https://burn-rs.github.io.
What are some alternatives?
Universal-G-Code-Sender - A cross-platform G-Code sender for GRBL, Smoothieware, TinyG and G2core.
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
tch-rs - Rust bindings for the C++ api of PyTorch.
bCNC - GRBL CNC command sender, autoleveler and g-code editor
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
gsender - Connect to and control Grbl-based CNCs with ease
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
cncjs - A web-based interface for CNC milling controller running Grbl, Marlin, Smoothieware, or TinyG.
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust
cncjs-kt-ext - Auto-leveling extension for CNCjs
wgpu - Cross-platform, safe, pure-rust graphics api.