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, dtype="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
```

lets scale this further

by youngbrett48 - opened Jan 29

Discussion

youngbrett48

Jan 29

I want to scale this concept to larger context with more tasks. I have started with a basic training run at 160 latent tokens on 5k research papers for the task of summarization. I believe it needs for data and more compute (or possibly just bigger models and more latent tokens), as it hallucinates a lot. This takes around 4 hours to train on a single h100 for 1 epoch. Here's the code and the model:

https://github.com/bdytx5/context_cascade
https://huggingface.co/youngbrett48/C3-Context-Cascade-Summarization

Let me know if you would like to collaborate further to push this concept to its potential.

Brett Young

youngbrett48

Jan 29

Here's a writeup on the project too: https://wandb.ai/byyoung3/c3-evaluation/reports/Testing-out-Context-Cascade-Compression-The-future-of-long-context-LLM-s---VmlldzoxNTc3MzU1Mw

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