RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it’s 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the “GPT” mode to quickly compute the hidden state for the “RNN” mode.
So it’s combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, “infinite” ctx_len, and free sentence embedding (using the final hidden state).
Raven 14B (finetuned on Alpaca+ShareGPT+…) Demo: https://huggingface.co/spaces/BlinkDL/ChatRWKV-gradio
Raven 7B (finetuned on Alpaca+ShareGPT+…) Demo: https://huggingface.co/spaces/BlinkDL/Raven-RWKV-7B
ChatRWKV: with “stream” and “split” strategies and INT8. 3G VRAM is enough to run RWKV 14B :) https://github.com/BlinkDL/ChatRWKV
Download RWKV-4 0.1⁄0.4⁄1.5/3/7/14B weights: https://huggingface.co/BlinkDL
RWKV pip package: https://pypi.org/project/rwkv/
``` os.environ["RWKV_JIT_ON"] = '1' os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster) from rwkv.model import RWKV # pip install rwkv model = RWKV(model='/fsx/BlinkDL/HF-MODEL/rwkv-4-pile-1b5/RWKV-4-Pile-1B5-20220903-8040', strategy='cuda fp16')
out, state = model.forward([187, 510, 1563, 310, 247], None) # use 20B_tokenizer.json print(out.detach().cpu().numpy()) # get logits out, state = model.forward([187, 510], None) out, state = model.forward(, state) # RNN has state (use deepcopy if you want to clone it) out, state = model.forward([310, 247], state) print(out.detach().cpu().numpy()) # same result as above
Cool Community RWKV Projects (check them!):
https://github.com/harrisonvanderbyl/rwkv-cpp-cuda pure CUDA RWKV (no need for python & pytorch)
https://github.com/Blealtan/RWKV-LM-LoRA LoRA fine-tuning
More RWKV projects: https://github.com/search?o=desc&q=rwkv&s=updated&type=Repositories
Visit Official Website