RWKV7-G1j-20260831

The Transformers integration, conversion, release packaging, linear-time RWKV tokenizer, and optional TileLang inference implementation are distributed with this release.

Highlights

• Constant recurrent state: memory does not grow like an attention KV cache.
• Bundled Transformers integration: auditable remote configuration and modeling modules provide generation, recurrent cache continuation, training, and LoRA workflows on Transformers 5.15+.
• Exact linear-time tokenizer: the bundled RWKV trie reads the self-contained tokenizer.json generated from the canonical RWKV World byte vocabulary.
• Chat-ready: chat_template.jinja supports system, multi-turn, thinking, and strict model-generated tool-call prompts.
• Optional optimized runtime: the isolated inference/ bundle provides PyTorch fallback and TileLang acceleration without changing the standard model root.

Model overview

Transformers quickstart

The repository includes configuration_rwkv7.py, modeling_rwkv7.py, and the exact linear-time tokenization_rwkv7.py. The model modules are adapted from the Transformers RWKV-7 integration at commit 4ad9ed0. Review those files and pin a model-repository revision in production. Passing trust_remote_code=True selects this bundled implementation even when the local Transformers installation also provides native RWKV-7 support.

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)

model_id = "RWKV/RWKV7-G1j-13.3B-20260831"
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
)

The recurrent cache returned by the model can be passed back for incremental decoding. Use an attention_mask for padded batches.

The model defaults to the chunk-parallel WKV path for efficient multi-token prefill. To reproduce the portable token-order reference path, set model.config.wkv_implementation = "eager" before the first forward pass. Chunked execution changes floating-point operation order, so small numerical differences from eager execution are expected.

Chat quickstart

import re

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

THINK_RE = re.compile(r"\A?\s*(.*?)\s*?", re.DOTALL)

def assistant_content(completion, thinking, *, close_incomplete=False):
    prefix = "\n"
    reply = prefix + completion
    thinking_block = THINK_RE.match(reply)
    if thinking:
        if thinking_block is not None or not close_incomplete:
            return reply.strip()
        return f"{reply.rstrip()}\n".strip()
    return "" if thinking_block is None else reply[thinking_block.end():].strip()

model_id = "RWKV/RWKV7-G1j-13.3B-20260831"
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
).to("cuda")

messages = [{"role": "user", "content": "Explain why RWKV uses constant state."}]
thinking = False
max_new_tokens = 256
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    thinking=thinking,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(
    **inputs,
    max_new_tokens=max_new_tokens,
    do_sample=True,
    temperature=1.0,
    top_p=0.5,
    eos_token_id=0,
    pad_token_id=0,
    stop_strings=["\n\nUser:"],
    tokenizer=tokenizer,
)
completion = tokenizer.decode(
    output[0, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)
completion = completion.split("\n\nUser:", 1)[0]
reached_token_limit = output.shape[1] - inputs["input_ids"].shape[1] >= max_new_tokens
print(
    assistant_content(
        completion,
        thinking,
        close_incomplete=reached_token_limit,
    )
)

Set thinking=True for the RWKV thinking prefix. The intentional generation prefixes are Assistant: followed by a newline and Assistant: <think. Only the enabled thinking prefix intentionally leaves its opening tag incomplete. The post-processing above reconstructs that prefix before removing an empty thinking block or preserving an enabled one. If generation hits the token limit inside thinking, it closes the displayed block be