llm.f90
hidet
| llm.f90 | hidet | |
|---|---|---|
| 13 | 3 | |
| 48 | 615 | |
| - | 1.1% | |
| 8.4 | 8.8 | |
| about 2 months ago | about 1 month ago | |
| Fortran | Python | |
| MIT License | Apache License 2.0 |
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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
hidet
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karpathy/llm.c
Check out Hidet [1]. Not as well funded, but delivers Python based ML acceleration with GPU support (unlike Mojo).
[1] https://github.com/hidet-org/hidet
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A PyTorch Approach to ML Infrastructure
Thanks for your comment ! Current focus of optimizations is for Nvidia GPUs but others are in the works. Hidet comes with Hidet.Script which abstracts some of the CUDA struggles and may make the ML optimizations efforts easier to implement. It is still evolving so documentation is limited but here are some examples: https://github.com/hidet-org/hidet/tree/main/python/hidet/gr...
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Hidet: A Deep Learning Compiler for Efficient Model Serving
Hey @bructhemoose2 can you file an issue, we will try to fix it ASAP: https://github.com/hidet-org/hidet/issues
What are some alternatives?
rwkv.f90 - Port of the RWKV-LM model in Fortran (Back to the Future!)
ColossalAI - Making large AI models cheaper, faster and more accessible
neural-fortran - A parallel framework for deep learning
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
inference-engine - A deep learning library for use in high-performance computing applications in modern Fortran
Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic
fastGPT - Fast GPT-2 inference written in Fortran
mamba-minimal - Simple, minimal implementation of the Mamba SSM in one file of PyTorch.
llvm-project - The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
ClimaCore.jl - CliMA model dycore
mamba - The Fast Cross-Platform Package Manager
curated-transformers - 🤖 A PyTorch library of curated Transformer models and their composable components