C3-Context-Cascade-Compression

<a href="https://huggingface.co/papers/2511.15244">Context Cascade Compression: Exploring the Upper Limits of Text Compression</a></h3>

🌟GitHub | 📜Paper

Fanfan Liu, Haibo Qiu

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, [email protected], 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}
}