rwkv-architecturelisted
Install: claude install-skill r1z4x/tezgah
# RWKV - Receptance Weighted Key Value
## Quick start
RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).
**Installation**:
```bash
# Install PyTorch
pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121
# Install dependencies
pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade
# Install RWKV
pip install rwkv
```
**Basic usage** (GPT mode + RNN mode):
```python
import os
from rwkv.model import RWKV
os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # Use CUDA kernel for speed
# Load model
model = RWKV(
model='/path/to/RWKV-4-Pile-1B5-20220903-8040',
strategy='cuda fp16'
)
# GPT mode (parallel processing)
out, state = model.forward([187, 510, 1563, 310, 247], None)
print(out.detach().cpu().numpy()) # Logits
# RNN mode (sequential processing, same result)
out, state = model.forward([187, 510], None) # First 2 tokens
out, state = model.forward([1563], state) # Next token
out, state = model.forward([310, 247], state) # Last tokens
print(out.detach().cpu().numpy()) # Same logits as above!
```
## Common workflows
### Workflow 1: Text generation (streaming)
**Efficient token-by-token generation**:
```python
from rwkv.model import RWKV
from rwkv.utils import PIPELINE
model = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')
pipeline = PIPELINE(model, "20B_tokenizer.json")
# Initial prompt
prompt = "The future of AI is"
s