| llm.f90 | llm.c | |
|---|---|---|
| 13 | 5 | |
| 48 | 18,275 | |
| - | - | |
| 8.4 | 3.7 | |
| about 2 months ago | 1 day ago | |
| Fortran | Cuda | |
| MIT License | MIT License |
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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 :)
-
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
llm.c
- Llm.c State of the Union
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Ask HN: Yo Nephew, in E. Africa, wants to train an LLM with on disk Wikipedia
----------------------------------
https://github.com/karpathy/llm.c
It is only 1,000 lines of easy to read C code.
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llm.c is now down to 26.2ms/iteration, matching PyTorch
I'm not grasping the significance of this. I see a repo called llm.c, https://github.com/karpathy/llm.c, is the main selling point that PyTorch is a large download and this isn't... or are there other problems with PyTorch? I thought the appeal of PyTorch is its accessibility and documentation.
- Layernorm
- karpathy/llm.c
What are some alternatives?
rwkv.f90 - Port of the RWKV-LM model in Fortran (Back to the Future!)
richard - Richard is gaining power
neural-fortran - A parallel framework for deep learning
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
fortran-wringer-tests - A collection of non-portable Fortran usage, standard-conformant or otherwise