fla-hub/rwkv7-7.2B-g0

rwkv7-7.2B-g0

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This is RWKV-7 model under flash-linear attention format.

Model Details

Model Description

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• —Developed by: Bo Peng, Yu Zhang, Songlin Yang, Ruichong Zhang, Zhiyuan Li
• —Funded by: RWKV Project (Under LF AI & Data Foundation)
• —Model type: RWKV7
• —Language(s) (NLP): Multilingual
• —License: Apache-2.0
• —Parameter count: 7.2B
• —Tokenizer: RWKV World tokenizer
• —Vocabulary size: 65,536

Model Sources

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• —Repository: https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
• —Paper: https://arxiv.org/abs/2503.14456
• —Model: https://huggingface.co/BlinkDL/rwkv7-g1/resolve/main/rwkv7-g1-2.9b-20250519-ctx4096.pth

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Install flash-linear-attention and the latest version of transformers before using this model:

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

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> You can use this model just as any other HuggingFace models:

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-7.2B-g0', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-7.2B-g0', 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'