rwkv7-1.5B-g1
This is RWKV-7 model under flash-linear attention format.
Model Details
Model Description
- 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: 1.5B
- 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
- Model: https://huggingface.co/BlinkDL/rwkv7-g1/blob/main/rwkv7-g1-1.5b-20250429-ctx4096.pth
Uses
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
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-g1', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-1.5B-g1', 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'