Instructions to use liufanfanlff/C3-Context-Cascade-Compression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liufanfanlff/C3-Context-Cascade-Compression with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="liufanfanlff/C3-Context-Cascade-Compression", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("liufanfanlff/C3-Context-Cascade-Compression", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use liufanfanlff/C3-Context-Cascade-Compression with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liufanfanlff/C3-Context-Cascade-Compression"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liufanfanlff/C3-Context-Cascade-Compression",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/liufanfanlff/C3-Context-Cascade-Compression

SGLang

How to use liufanfanlff/C3-Context-Cascade-Compression with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "liufanfanlff/C3-Context-Cascade-Compression" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liufanfanlff/C3-Context-Cascade-Compression",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "liufanfanlff/C3-Context-Cascade-Compression" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liufanfanlff/C3-Context-Cascade-Compression",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use liufanfanlff/C3-Context-Cascade-Compression with Docker Model Runner:
```
docker model run hf.co/liufanfanlff/C3-Context-Cascade-Compression
```

Update model card: add pipeline, library tags, correct license, and fix paper link

by nielsr HF Staff - opened Nov 20, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +6 -5

README.md CHANGED Viewed

@@ -1,10 +1,10 @@
 ---
-license: mit
 ---
----
-license: mit
----
-<h3><a href="">Context Cascade Compression: Exploring the Upper Limits of Text Compression</a></h3>
 [🌟GitHub](https://github.com/liufanfanlff/C3-Context-Cascade-Compression) | [📜Paper](https://arxiv.org/abs/2511.15244)
@@ -60,3 +60,4 @@ Don't hesitate to contact me by email, liufanfan19@mails.ucas.ac.cn, if you have
   journal={arXiv preprint arXiv:2511.15244},
   year={2025}
 }

 ---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
+### <a href="https://huggingface.co/papers/2511.15244">Context Cascade Compression: Exploring the Upper Limits of Text Compression</a></h3>
 [🌟GitHub](https://github.com/liufanfanlff/C3-Context-Cascade-Compression) | [📜Paper](https://arxiv.org/abs/2511.15244)
   journal={arXiv preprint arXiv:2511.15244},
   year={2025}
 }
+```