ChatRWKV (pronounced as "RwaKuv", from 4 major params: R W K V)

ChatRWKV is like ChatGPT but powered by my RWKV (100% RNN) language model, which is the only RNN (as of now) that can match transformers in quality and scaling, while being faster and saves VRAM. Training sponsored by Stability EleutherAI :) 中文使用教程，请往下看，在本页面底部。

Raven 14B (finetuned on Alpaca+ShareGPT+...) Demo: https://huggingface.co/spaces/BlinkDL/ChatRWKV-gradio

World 7B (supports 100+ world languages) Demo: https://huggingface.co/spaces/BlinkDL/RWKV-World-7B

Download RWKV-4 weights: https://huggingface.co/BlinkDL (Use RWKV-4 models. DO NOT use RWKV-4a and RWKV-4b models.)

Note: RWKV-4-World is the best model: generation & chat & code in 100+ world languages, with the best English zero-shot & in-context learning ability too.

Use v2/convert_model.py to convert a model for a strategy, for faster loading & saves CPU RAM.

Note RWKV_CUDA_ON will build a CUDA kernel (much faster & saves VRAM). Here is how to build it ("pip install ninja" first):

# How to build in Linux: set these and run v2/chat.py
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# How to build in win:
Install VS2022 build tools (https://aka.ms/vs/17/release/vs_BuildTools.exe select Desktop C++). Reinstall CUDA 11.7 (install VC++ extensions). Run v2/chat.py in "x64 native tools command prompt".

RWKV pip package: https://pypi.org/project/rwkv/ (please always check for latest version and upgrade)

World demo script: https://github.com/BlinkDL/ChatRWKV/blob/main/API_DEMO_WORLD.py

Raven Q&A demo script: https://github.com/BlinkDL/ChatRWKV/blob/main/v2/benchmark_more.py

RWKV Discord: https://discord.gg/bDSBUMeFpc (let's build together)

Twitter: https://twitter.com/BlinkDL_AI

RWKV LM: https://github.com/BlinkDL/RWKV-LM (explanation, fine-tuning, training, etc.)

RWKV in 150 lines (model, inference, text generation): https://github.com/BlinkDL/ChatRWKV/blob/main/RWKV_in_150_lines.py

Building your own RWKV inference engine: begin with https://github.com/BlinkDL/ChatRWKV/blob/main/src/model_run.py which is easier to understand (used by https://github.com/BlinkDL/ChatRWKV/blob/main/chat.py).

RWKV preprint https://arxiv.org/abs/2305.13048

Cool Community RWKV Projects:

https://github.com/saharNooby/rwkv.cpp fast i4 i8 fp16 fp32 CPU inference using ggml

https://github.com/harrisonvanderbyl/rwkv-cpp-cuda fast windows/linux & cuda/rocm/vulkan GPU inference (no need for python & pytorch)

https://github.com/Blealtan/RWKV-LM-LoRA LoRA fine-tuning

https://github.com/josStorer/RWKV-Runner cool GUI

More RWKV projects: https://github.com/search?o=desc&q=rwkv&s=updated&type=Repositories

ChatRWKV v2: with "stream" and "split" strategies, and INT8. 3G VRAM is enough to run RWKV 14B :) https://github.com/BlinkDL/ChatRWKV/tree/main/v2

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([1563], 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

Here is https://huggingface.co/BlinkDL/rwkv-4-raven/blob/main/RWKV-4-Raven-14B-v7-Eng-20230404-ctx4096.pth in action:

When you build a RWKV chatbot, always check the text corresponding to the state, in order to prevent bugs.

Never call raw forward() directly. Instead, put it in a function that will record the text corresponding to the state.
The best chat format (check whether your text is of this format): Bob: xxxxxxxxxxxxxxxxxx\n\nAlice: xxxxxxxxxxxxx\n\nBob: xxxxxxxxxxxxxxxx\n\nAlice:

There should not be any space after the final "Alice:". The generation result will have a space in the beginning, and you can simply strip it.
You can use \n in xxxxx, but avoid \n\n. So simply do xxxxx = xxxxx.strip().replace('\r\n','\n').replace('\n\n','\n')

If you are building your own RWKV inference engine, begin with https://github.com/BlinkDL/ChatRWKV/blob/main/src/model_run.py which is easier to understand (used by https://github.com/BlinkDL/ChatRWKV/blob/main/chat.py)

The lastest "Raven"-series Alpaca-style-tuned RWKV 14B & 7B models are very good (almost ChatGPT-like, good at multiround chat too). Download: https://huggingface.co/BlinkDL/rwkv-4-raven

Previous old model results: