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

Libraries

How to use EleutherAI/deep-ignorance-e2e-strong-filter-cb 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-cb")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

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

-27

nielsr

Aug 13, 2025

This PR enhances the model card by:

Adding the pipeline_tag: text-generation to the metadata, which improves discoverability on the Hugging Face Hub (e.g., https://huggingface.co/models?pipeline_tag=text-generation).
Specifying library_name: transformers in the metadata to enable the "How to use" widget on the model page, given the model's compatibility with the Transformers library.
Adding explicit links to the paper, project page, and GitHub repository at the top of the README content for easy access and navigation.
Updating the existing paper link within the introductory text to point to the official Hugging Face Papers page.

These changes provide more comprehensive and accessible information for users interacting with the model.

Improve model card: Add pipeline tag, library name, and explicit links2b0f70db

Kyle1668 changed pull request status to merged Aug 13, 2025

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