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RWKV: RNN with Transformer-level LLM Performance
It combines 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).
https://github.com/BlinkDL/RWKV-LM
https://github.com/BlinkDL/ChatRWKV
Using RWKV in the web UI
1. Download the model
It is available in different sizes:
- https://huggingface.co/BlinkDL/rwkv-4-pile-3b/
- https://huggingface.co/BlinkDL/rwkv-4-pile-7b/
- https://huggingface.co/BlinkDL/rwkv-4-pile-14b/
There are also older releases with smaller sizes like:
Download the chosen .pth and put it directly in the models folder.
2. Download the tokenizer
Also put it directly in the models folder. Make sure to not rename it. It should be called 20B_tokenizer.json.
3. Launch the web UI
No additional steps are required. Just launch it as you would with any other model.
python server.py --listen --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth
Setting a custom strategy
It is possible to have very fine control over the offloading and precision for the model with the --rwkv-strategy flag. Possible values include:
"cpu fp32" # CPU mode
"cuda fp16" # GPU mode with float16 precision
"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation.
"cuda fp16i8" # GPU mode with 8-bit precision
See the README for the PyPl package for more details: https://pypi.org/project/rwkv/
Compiling the CUDA kernel
You can compile the CUDA kernel for the model with --rwkv-cuda-on. This should improve the performance a lot but I haven't been able to get it to work yet.