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RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Offi
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill rwkv -a cursor -y
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).
Installation:
# 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):
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!
Efficient token-by-token generation:
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"
state = None
# Generate token by token
for token in prompt:
out, state = pipeline.model.forward(pipeline.encode(token), state)
# Continue generation
for _ in range(100):
out, state = pipeline.model.forward(None, state)
token = pipeline.sample_logits(out)
print(pipeline.decode(token), end='', flush=True)
Key advantage: Constant memory per token (no growing KV cache)
Process million-token sequences:
model = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')
# Process very long document
state = None
long_document = load_document() # e.g., 1M tokens
# Stream through entire document
for chunk in chunks(long_document, chunk_size=1024):
out, state = model.forward(chunk, state)
# State now contains information from entire 1M token document
# Memory usage: O(1) (constant, not O(n)!)
Standard fine-tuning workflow:
# Training script
import pytorch_lightning as pl
from rwkv.model import RWKV
from rwkv.trainer import RWKVTrainer
# Configure model
config = {
'n_layer': 24,
'n_embd': 1024,
'vocab_size': 50277,
'ctx_len': 1024
}
# Setup trainer
trainer = pl.Trainer(
accelerator='gpu',
devices=8,
precision='bf16',
strategy='deepspeed_stage_2',
max_epochs=1
)
# Train
model = RWKV(config)
trainer.fit(model, train_dataloader)
Memory comparison (1M token sequence):
# Transformer (GPT)
# Memory: O(n²) for attention
# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)
# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)
# RWKV
# Memory: O(1) per token
# State: hidden_dim × n_layers = 4096 × 24 = ~400KB
# 1,000,000× more efficient!
Speed comparison (inference):
# Transformer: O(n) per token (quadratic overall)
# First token: 1 computation
# Second token: 2 computations
# ...
# 1000th token: 1000 computations
# RWKV: O(1) per token (linear overall)
# Every token: 1 computation
# 1000th token: 1 computation (same as first!)
Use RWKV when:
Key advantages:
Use alternatives instead:
Issue: Out of memory during training
Use gradient checkpointing and DeepSpeed:
trainer = pl.Trainer(
strategy='deepspeed_stage_3', # Full ZeRO-3
precision='bf16'
)
Issue: Slow inference
Enable CUDA kernel:
os.environ["RWKV_CUDA_ON"] = '1'
Issue: Model not loading
Check model path and strategy:
model = RWKV(
model='/absolute/path/to/model.pth',
strategy='cuda fp16' # Or 'cpu fp32' for CPU
)
Issue: State management in RNN mode
Always pass state between forward calls:
# WRONG: State lost
out1, _ = model.forward(tokens1, None)
out2, _ = model.forward(tokens2, None) # No context from tokens1!
# CORRECT: State preserved
out1, state = model.forward(tokens1, None)
out2, state = model.forward(tokens2, state) # Has context from tokens1
Time-mixing and channel-mixing: See references/architecture-details.md for WKV operation, time-decay mechanism, and receptance gates.
State management: See references/state-management.md for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.
RWKV-7 improvements: See references/rwkv7.md for latest architectural improvements (March 2025) and multimodal capabilities.
Performance (vs Transformers):