Text Generation
Transformers
Safetensors
PyTorch
English
gpt_neox
causal-lm
pythia
safety
unlearning
data-filtering
interpretability
pretraining
eleutherai
gpt-neox
wmdp
cbrn
tamper-resistance
research
model-suite
6.9b
circuit-breaking
knowledge-filtering
open-weight
biothreat
safety-research
model-diffing
training-dynamics
text-generation-inference
Instructions to use EleutherAI/deep-ignorance-e2e-weak-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EleutherAI/deep-ignorance-e2e-weak-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EleutherAI/deep-ignorance-e2e-weak-filter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/deep-ignorance-e2e-weak-filter") model = AutoModelForCausalLM.from_pretrained("EleutherAI/deep-ignorance-e2e-weak-filter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EleutherAI/deep-ignorance-e2e-weak-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-weak-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-weak-filter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EleutherAI/deep-ignorance-e2e-weak-filter
- SGLang
How to use EleutherAI/deep-ignorance-e2e-weak-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-weak-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-weak-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-weak-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-weak-filter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EleutherAI/deep-ignorance-e2e-weak-filter with Docker Model Runner:
docker model run hf.co/EleutherAI/deep-ignorance-e2e-weak-filter
Commit History
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Improve model card: Add pipeline tag, library name, and links
Verified signatureThis PR enhances the model card by: - Adding the `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 indicate compatibility with the Hugging Face Transformers library, enabling the "how to use" widget. - Updating the primary paper link to point to the official Hugging Face Papers page (https://huggingface.co/papers/2508.06601). - Adding explicit links to the project page and the GitHub repository in the content section. - Making a minor improvement to the quickstart code by adding a `print()` statement for the decoded output. Please review and merge if these improvements are satisfactory!f8ce77a
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