Currently being rewritten in the rewrite branch.
A port of the RWKV-LM family of large language models to the tinygrad framework.
- Implement the WKV kernel as a custom function
- Implement the backwards of the WKV kernel as a custom function
- Add support for the world model and tokenizer
- Add support for the MIDI models
- Add initial support for RWKV-5 models
Currently, requires tinygrad from git or just use the nix flake.
numpy
pydot (only for GRAPH=1)
tinygrad
tokenizers
torch (only for loading pytorch weights)
tqdm
wandb (optional during training)
rust (only for compiling)
clang (only for compiling)
graphviz (only for GRAPH=1)
Run the CLI with python -m cli.
Also, usable as a python package to embed in other projects. It's also possible to compile the model to portable C code and embed it that way.
usage: tinyrwkv-cli [-h] [--seed SEED] {pre,gen,cht,cmp,bch,ptr,gpt,tra,bpt,wkv,mus} ...
CLI for tinyrwkv
positional arguments:
{pre,gen,cht,cmp,bch,ptr,gpt,tra,bpt,wkv,mus}
pre preprocess either tinyrwkv trained weights or pytorch trained weights into RNN form
gen freeform generation using the RNN mode (requires a preprocessed model using `pre`)
cht chat with a model in RNN mode (requires a preprocessed model using `pre`)
cmp compile a RNN model into c source code and a compiled executable (need to run with CLANG=1)
bch benchmark the rnn mode
ptr preprocess pytorch weights weights into GPT form for training or inference
gpt freeform generation using the GPT mode (requires a preprocessed model using `ptr`)
tra pretrain or finetune a model (if finetuning the model needs to be preprocessed with `ptr`)
bpt benchmark the gpt mode
wkv benchmark/test each wkv module
mus music generation using the RNN mode (requires a preprocessed model using `pre`)
options:
-h, --help show this help message and exit
--seed SEED seed for random