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fla-hub/
rwkv7-0.1B-g1a

Name: rwkv7-0.1B-g1a
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rwkv7-0.1B-g1a

This is RWKV-7 g1 model under flash-linear attention format. The g1 model series added significant more data and incorporated deep thinking abilities.

Model Details

Model Description

Developed by: Bo Peng, Yu Zhang, Songlin Yang, Ruichong Zhang
Funded by: RWKV Project (Under LF AI & Data Foundation)
Model type: RWKV7
Language(s) (NLP): Multilingal
License: Apache-2.0
Parameter count: 191M
Tokenizer: RWKV World tokenizer
Vocabulary size: 65,536

Model Sources

Repository: https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
Paper: https://arxiv.org/abs/2503.14456

Uses

Install flash-linear-attention and the latest version of transformers before using this model:

bash

pip install git+https://github.com/fla-org/flash-linear-attention
pip install 'transformers>=4.48.0'

Direct Use

You can use this model just as any other HuggingFace models:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-0.1B-g1a', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-0.1B-g1a', trust_remote_code=True)
model = model.cuda() # Supported on Nvidia/AMD/Intel eg. model.xpu()
prompt = "What is a large language model?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Default is True, set to False to disable thinking
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=1.0,
    top_p=0.3,
    repetition_penalty=1.2
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
print(response)

FAQ

Q: safetensors metadata is none.

A: upgrade transformers to >=4.48.0: pip install 'transformers>=4.48.0'

Thinking Prompt

<|rwkv_tokenizer_end_of_text|>User: <Your Question Here>

Assistant: <think

Don't close the brackets for <think!

Addidtional Caveats for Prompting

Always add <|rwkv_tokenizer_end_of_text|> (Token ID = 0) before your prompt. The model is incapable of attending the first token it receives due to state initialization issues.

Bad prompt example:

Mathews lifted a dark brow. "Are you sure about that? I mean, wouldn't it be better to wait until Dale is home safe and sound?"

"The longer I wait to tell her, the worse it will be for both of us."

"Good luck. You're going to need it," said

The model is unable to recall Mathews because it is the very first token of the input.

Good prompt example:

<|rwkv_tokenizer_end_of_text|>Mathews lifted a dark brow. "Are you sure about that? I mean, wouldn't it be better to wait until Dale is home safe and sound?"

"The longer I wait to tell her, the worse it will be for both of us."

"Good luck. You're going to need it," said

the model will output Mathews as expected.

Without this token: lambada_openai ppl=13.84 acc=48.13%

With this token added: lambada_openai ppl=12.36 acc=49.12%

Note: this phenomenon is very rare for Transformers but significant for RNNs. We speculate that the model uses the first token to pin the states, to better acquire information from later tokens.