rwkv7-1.5B-world

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

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): English, Chinese, Japanese, Korean, French, Arabic, Spanish, Portuguese
• License: Apache-2.0
• Parameter count: 1.52B
• 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://huggingface.co/papers/2503.14456

Uses

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

pip install flash-linear-attention==0.3.0
pip install 'transformers>=4.48.0'

Direct Use

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

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-1.5B-world', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-1.5B-world', 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
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096,
    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)

Training Details

Training Data

This model is trained on the World v3 with a total of 3.119 trillion tokens.

Training Hyperparameters

• Training regime: bfloat16, lr 4e-4 to 1e-5 "delayed" cosine decay, wd 0.1 (with increasing batch sizes during the middle)
• Final Loss: 1.9965
• Token Count: 3.119 trillion

Evaluation

Metrics

lambada_openai:

before conversion: ppl 4.13 acc 69.4%

after conversion: ppl 4.26 acc 68.8% (without apply temple)

FAQ

Q: safetensors metadata is none.

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