candle
llm.f90
candle | llm.f90 | |
---|---|---|
17 | 13 | |
13,475 | 48 | |
4.4% | - | |
9.9 | 8.4 | |
4 days ago | about 2 months ago | |
Rust | Fortran | |
Apache License 2.0 | MIT License |
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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
llm.f90
- llm.f90: LLM Inference in Fortran
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karpathy/llm.c
I'd like to think he took the name from my llm.f90 project https://github.com/rbitr/llm.f90
It was originally based off of Karpathy's llama2.c but I renamed it when I added support for other architectures.
Probable a coincidence :)
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Winteracter – The Fortran GUI Toolset
I'm a Fortran hobbyist. I'm working (unfortunately less frequently now) on a LLM framework in Fortan: https://github.com/rbitr/llm.f90
- Fortran implementation of phi-2 LLM
- Fortran implementation of phi-2 language model
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TinyLlama: An Open-Source Small Language Model
Also, I should promote the code I wrote for running this. It runs models in ggml format, the one I made available is an older checkpoint though. It's easy to convert the newer one. And it's in Fortran but it should be easy to get gfortran if you don't have it installed.
https://github.com/rbitr/llm.f90/tree/optimize16/purefortran
- Mamba LLM Inference on CPU
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Minimal implementation of Mamba, the new LLM architecture, in 1 file of PyTorch
The original mamba code has a lot of speed optimizations and other stuff that make it difficult to immediately get so this will help with learning.
I can't help but also plug my own Mamba inference implementation. https://github.com/rbitr/llm.f90/tree/master/ssm
- Mamba state-space LLM inference
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Guide to the Mamba architecture that claims to be a replacement for Transformers
You may also be interested in https://github.com/rbitr/llm.f90/tree/master/ssm it's my inference only implementation of mamba which ends up being much simpler than the training code in the original repo
What are some alternatives?
Universal-G-Code-Sender - A cross-platform G-Code sender for GRBL, Smoothieware, TinyG and G2core.
rwkv.f90 - Port of the RWKV-LM model in Fortran (Back to the Future!)
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]
neural-fortran - A parallel framework for deep learning
tch-rs - Rust bindings for the C++ api of PyTorch.
inference-engine - A deep learning library for use in high-performance computing applications in modern Fortran
bCNC - GRBL CNC command sender, autoleveler and g-code editor
fastGPT - Fast GPT-2 inference written in Fortran
gsender - Connect to and control Grbl-based CNCs with ease
mamba-minimal - Simple, minimal implementation of the Mamba SSM in one file of PyTorch.
cncjs - A web-based interface for CNC milling controller running Grbl, Marlin, Smoothieware, or TinyG.
Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic