metadata

base_model:
  - BlinkDL/rwkv-7-world
language:
  - en
  - zh
  - ja
  - ko
  - fr
  - ar
  - es
  - pt
license: apache-2.0
metrics:
  - accuracy
pipeline_tag: text-generation
library_name: transformers

rwkv7-191M-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
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:

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/rwkv7-191M-world', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-191M-world', trust_remote_code=True)
model = model.cuda()
prompt = "What is a large language model?"
messages = [
    {"role": "user", "content": "Who are you?"},
    {"role": "assistant", "content": "I am a GPT-3 based model."},
    {"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=1024,
)
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 v2.8 with a total of 1.0 trillion tokens.

Training Hyperparameters

Training regime: bfloat16, lr 8e-4 to 1e-5 "delayed" cosine decay, wd 0.1, bsz 8x30x4096 (Please contact Peng Bo for details)

Evaluation

Metrics

lambada_openai: ppl 12.4 acc 49.1%

FAQ

Q: safetensors metadata is none.

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