Linear RNN/Reservoir hybrid generative model, one C file (no deps.)

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47710713

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HN

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Linear RNN/Reservoir hybrid generative model, one C file (no deps.)

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githubusercontent.com

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April 9, 2026

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7

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Fetched at

Sept. 7, 2026, 9:26 p.m.

Updated at

Sept. 7, 2026, 9:26 p.m.

Description

I just noticed it takes literally ~5 minutes to train millions parameters on slow CPU...but before you call Yudkowsky that "it's over", an important note: the main bottleneck is the corpus size, params are just 'cleverness' but given limited info it's powerless.Anyway, here is the project:https://github.com/bggb7781-collab/lrnnsmdds/tree/maincouple of notes:1. single C file, no dependencies. Below are literally all the "dependencies", not even custom header (copy paste from the top of the single c file):#define _POSIX_C_SOURCE 200809L#include <stdio.h> #include <stdlib.h> #include <string.h> #include <math.h> #include <time.h> #include <stdint.h> #include <stdbool.h> #include <float.h> #include <getopt.h> #include <errno.h>4136 lines of code in one file at the moment, that's all.2. easiest way to compile on Windows: download Cygwin (https://www.cygwin.com/), then navigate to the directory where your lrnnsmdds.c file is and just run gcc on it with some optimizations, such as:gcc -std=c17 -O3 -march=native --fast-math -o lrnn lrnnsmdds.c -lmOn Linux just run gcc, if for whatever reason you don't have gcc on Linux do sudo && apt-get install gcc --y ,or something...On Apple: i've no idea or maybe just use vmware and install ubuntu and then run it.Of course you can 'git clone' and go to the dir, but again: it's one file! copy it...The repo has tiny toy corpus included where i've borrowed (hopefully it's not plagiarism!) the name "John Gordon" from one of my favorite books "Star Kings", by E. Hamilton. Just the first and last name are copied, the content is unique (well several poorly written sentences by myself...). Obviously it will overfit and result on copy-paste on such small corpus, the sole goal is to check if everything runs and not if it's the A-G-I. You'd need your own 100kb+ if you want to generate unique meaningful text.3. why/what/when/how?The github repo is self-explanatory i believe about features, uses and goals but in an attempt to summarize:My main motivation was to create a fast alternative to transformers which works on CPU only, hence you see the bizarre/not-easy task of doing this in C and not python and the lack of dependencies. In addition I was hoping it will also be clever alternative hence you see all those features more stacked than 90s BMW 850. The 'reservoir' is the most novel feature though, it offers quick exact recall arguably different than RWKV 8 or the latest Mamba, in fact name of the architecture SMDDS comes from the first letters of the implemented features:* S. SwiGLU in Channel Mixing (more coherence) * M. Multi-Scale Token Shift (larger context) * D. Data-Dependent Decay with Low-Rank (speed in large context) * D. Dynamic State Checkpointing (faster/linear generation) * S. Slot-memory reservoir (perfect recall, transformers style).If you face some issue just email me (easiest).the good, the bad the ugly:It is more or less working text-to-text novel alternative architecture, it's not trying to imitate transformers nor LSTM, Mamba, RWKV though it shares many features with them - the bad is that it's not blazing fast, if you're armed with ryzen/i7 16 cores or whatever and patience you can try training it on several small books via word tokenizer and low perplexity (under 1.2...) and see if it looks smarter/faster. Since this is open source obviously the hope is to be improved: make it cuda-friendly, improve the features, port to python etc.Depending on many factors I may try to push for v2 in July, August, September. My focus at the moment will be to test and scale since the features are many, it compiles with zero warnings on the 2 laptops i've tested(windows/cygwin and ubuntu) and the speed is comparable to transformers. 10x!

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