Instructions to use EleutherAI/deep-ignorance-e2e-strong-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EleutherAI/deep-ignorance-e2e-strong-filter with Transformers:

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

pipe = pipeline("text-generation", model="EleutherAI/deep-ignorance-e2e-strong-filter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/deep-ignorance-e2e-strong-filter")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/deep-ignorance-e2e-strong-filter")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EleutherAI/deep-ignorance-e2e-strong-filter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EleutherAI/deep-ignorance-e2e-strong-filter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EleutherAI/deep-ignorance-e2e-strong-filter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EleutherAI/deep-ignorance-e2e-strong-filter

SGLang

How to use EleutherAI/deep-ignorance-e2e-strong-filter 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 "EleutherAI/deep-ignorance-e2e-strong-filter" \
    --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": "EleutherAI/deep-ignorance-e2e-strong-filter",
		"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 "EleutherAI/deep-ignorance-e2e-strong-filter" \
        --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": "EleutherAI/deep-ignorance-e2e-strong-filter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EleutherAI/deep-ignorance-e2e-strong-filter with Docker Model Runner:
```
docker model run hf.co/EleutherAI/deep-ignorance-e2e-strong-filter
```

Improve model card: Add pipeline tag, library name, and explicit links

by nielsr HF Staff - opened Aug 13, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+31

-28

nielsr

Aug 13, 2025

This PR enhances the model card by:

Adding pipeline_tag: text-generation to improve discoverability on the Hugging Face Hub (e.g., https://huggingface.co/models?pipeline_tag=text-generation).
Specifying library_name: transformers to ensure the correct usage badge appears and facilitates integration with the transformers library, as shown in the quickstart code.
Adding explicit links to the Hugging Face paper page (Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs), the project page (https://deepignorance.ai/), and the GitHub repository (https://github.com/EleutherAI/deep-ignorance) for easier access to relevant resources.
Removing the redundant inline paper link in the introductory paragraph, as the prominent links at the top suffice.
Adding a print() statement to the sample usage code for immediate output visibility.

Improve model card: Add pipeline tag, library name, and explicit linksbd55c40a

Kyle1668 changed pull request status to merged Aug 13, 2025

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