Context Cascade Compression: Exploring the Upper Limits of Text Compression
Usage
from transformers import AutoModel, AutoTokenizer
model_name = 'liufanfanlff/C3-Context-Cascade-Compression'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name , trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
model = model.eval().cuda()
prompt = 'Repeat the text: '
context = "帝高阳之苗裔兮，朕皇考曰伯庸。摄提贞于孟陬兮，"
#context = "lfflfflfflfflfflfflfflfflff"
outputs = model.chat(tokenizer, context, prompt)
print ("Repeat the text: ",outputs)
viz
Contact
Don't hesitate to contact me by email, liufanfan19@mails.ucas.ac.cn, if you have any questions.
Acknowledgement
- DeepSeek-OCR: the idea originated from reconsideration of this work.
- GOT-OCR2.0: the code was adapted from GOT-OCR2.0.
- Qwen: the LLM base model of C3.
Citation
@article{liu2025context,
title={Context Cascade Compression: Exploring the Upper Limits of Text Compression},
author={Liu, Fanfan and Qiu, Haibo},
journal={arXiv preprint arXiv:2511.15244},
year={2025}
}