base_model:

• EleutherAI/deep-ignorance-pretraining-stage-unfiltered
  datasets:
• EleutherAI/deep-ignorance-pretraining-mix
• EleutherAI/deep-ignorance-annealing-mix
  language:
• en
  license: apache-2.0
  library_name: transformers
  pipeline_tag: text-generation
  tags:
• pytorch
• 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

Deep Ignorance Model Suite

📄 Paper | 🌐 Project Page | 💻 Code

We explore an intuitive yet understudied question: Can we prevent LLMs from learning unsafe technical capabilities (such as CBRN) by filtering out enough of the relevant pretraining data before we begin training a model? Research into this question resulted in the Deep Ignorance Suite. In our experimental setup, we find that filtering pretraining data prevents undesirable knowledge, doesn't sacrifice general performance, and results in models that are resistant to tampering.

Deep Ignorance is a collection of 6.9B models developed to facilitate research into pretraining, interpretability, training data, and unlearning (see project page). It contains 18 models composing of a baseline model trained on unfiltered data, and 17 models trained on filtered datasets or with other safety interventions being applied. Pretraining stage models have 101 checkpoints and annealing stage have 11.

Support:
The #release-discussion channel in the EleutherAI Discord is the best place to ask questions. Questions asked in other channels are less likely to be answered. The community section on HuggingFace is less actively monitored. Tag Kyle O'Brien in the EleutherAI Discord for faster response times.

Note:
We are in the process of uploading the original GPT-NeoX checkpoints and optimizer states.

Research

Our research and model suite open up multiple avenues for future work. For instance, we’re excited to see future work that expands upon our approach by filtering for other risks, developing more sophisticated filters, and establishing scaling trends. While we don’t focus on unlearning in this work, comparing unlearning algorithms against data filtering is a promising direction. Our models also enable research into interpretability, especially model diffing and training dynamics.

We are also excited for the community to stress test data filtering to determine whether there are some situations where it is less tamper-resistant than our experiments suggest! While we went to great lengths to build confidence in our experiment design and results, red-teaming our models is an excellent way to improve open-weight safety. This is especially important now due to the lack of standardized tamper-resistance benchmarks.

Uses and Limitations

Quickstart

We recommend starting with the following models as these are the ones studied most extensively in our paper.

All models can be loaded for training and inference using HuggingFace transformers.

from transformers import GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained(
  "EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal",
  revision="global_step11921",
)

tokenizer = AutoTokenizer.from_pretrained(
  "EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal",
  revision="global_step11921",
)

inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
print(tokenizer.decode(tokens[0]))

Revision/branch global_step11921 corresponds exactly to the model checkpoint on the main branch of each model. Specifying the revision allows you to load intermediate checkpoints. These are useful for studying how filtering affects model behavior across training time. Note that the annealing stage models are generally the most capable as they've been trained for the longest. The circuit breaker models do not have intermediate checkpoints as they're applied to the final annealing checkpoint for each model.

Full Model List

Intended Use

Deep Ignorance is primarily intended for research into the behavior, functionality, and limitations of large language models, providing a controlled setting for conducting scientific experiments, with intermediate checkpoints for most models made available as branches hosted on Hugging Face.

Deep Ignorance models have not undergone any post-training. They often fall into repetition. They do not follow user instructions. Structured benchmarks work best for evaluating them. Applying post-training to these models could be valuable future work.

Out-of-scope use

The Deep Ignorance Suite is not intended for deployment and is not a product for human-facing interactions. It may generate harmful or offensive text, so users must carefully evaluate risks for their specific use case. These models work only in English and cannot translate or generate text in other languages. They have not been fine-tuned for common uses like writing prose or powering commercial chatbots. Unlike ChatGPT, Deep Ignorance will not respond to prompts as expected because it lacks fine-tuning through methods like Reinforcement Learning from Human Feedback (RLHF).

Training

All of our models undergo identical pretraining and annealing setups except for some data being removed by filters. All other hyperparameters are identical. This allows practitioners to make causal claims about data filtering's impact on training dynamics and behavior. Models trained on filtered datasets are trained for a little more than one epoch until they reach 550B training tokens in total.

Training data

Pretraining: We utilize a deduplicated version of DCLM provided by ZyphraAI as our pretraining dataset. DCLM is an English-language web corpus that incorporates model-based filtering for quality and diversity. It has demonstrated success in training high-performing open-source language models. Our implementation uses approximately 500B tokens with the GPT-NeoX tokenizer, encompassing 409,935,485 documents.

Annealing/Midtraining: Following pretraining, we perform an annealing phase with an additional 50B high-quality tokens. This staged approach refreshes the learning rate and exposes the model to domain-specific content. Our annealing mixture allocates 25B tokens (50%) to previously unseen DCLM data and 25B tokens to specialized content. The domain-specific portion emphasizes scientific and instructional data, including Flan (16.87%), StackExchange (2.82%), Pes2o (22.90%), Wikipedia (7.37%), and small amounts of Camel Bio, Chemistry, and Physics datasets (0.02% each). This composition targets improvements in knowledge benchmarks while maintaining broad capabilities.

Evaluations

We evaluate our models across two primary dimensions: (1) retention of general capabilities and (2) reduction of biothreat proxy knowledge. This dual evaluation approach ensures that our filtering techniques effectively remove unwanted knowledge while preserving beneficial capabilities.

Biothreat Proxy Knowledge Benchmarks

We assess biothreat-related knowledge using the WMDP-Bio benchmark, focusing on two robust evaluation formats designed to minimize shortcut exploitation:

WMDP-Bio Robust MCQA (868 Questions): A curated subset of the original WMDP-Bio benchmark that excludes questions vulnerable to heuristic exploitation. We removed 405 questions (31.81%) where three different models could correctly answer based solely on the answer choices without seeing the question text. This subset provides a more reliable assessment of genuine biothreat proxy knowledge.

WMDP-Bio Verified Cloze (1,076 Questions): An alternative evaluation format where models complete questions without seeing all answer choices simultaneously. We evaluate the length-normalized log probability of each answer separately, preventing models from using comparative heuristics between choices. Questions incompatible with cloze-style evaluation (e.g., "All of the above" or "Which of the following is most...") are excluded.

General Capability Benchmarks

To ensure our filtering approach preserves beneficial knowledge, we evaluate on standard benchmarks:

• MMLU: Factual knowledge across diverse topics
• PIQA: Physical commonsense reasoning tasks
• LAMBADA: Text comprehension requiring full-context understanding
• HellaSwag: Commonsense natural language inference

Acknowledgments

This work was done in collaboration with the UK AI Security Institute and the University of Oxford.

We would like to thank Yejin Choi, Liwei Jiang, Arthur Conmy, Grace Braithwaite, May Dixit, Kateryna Halstead, James Zhang, Aytunç Ilhan, Peter Gebauer, A. Feder Cooper, Adam Gleave, Pietro Lesci, Ian McKenzie, Samuel Ratnam, Paul Rottger, Lydia O'Brien, Cameron Tice, Blake Bullwinkel, Nora Belrose, Patricia Paskov and Aviya Skowron for helpful discussions. Alex Robey and Alexandra Souly also provided valuable methodological input. Jai Patel coordinated collaboration logistics between EleutherAI and UK AISI. Iman Syed offered support related to compute behind our tampering experiments. Kyle O'Brien was partially supported financially by the Cambridge ERA:AI Fellowship.

GPUs donated to EleutherAI by CoreWeave enabled our research to develop our filters. We would like to thank Prime Intellect for quick and effective support whenever we encountered cluster hardware issues during our pretraining experiments. Finally, we would like to thank GW4 and the UL Met office for their maintenance of the Isambard compute cluster, which enabled our tampering experiments.

Our README was inspired by the Pythia, Qwen, and OLMo2 model suites.

Citation

@article{obrien2025deepignorance,
    title={Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs},
    author={O'Brien, Kyle and Casper, Stephen and Anthony, Quentin and Korbak, Tomek and Kirk, Robert and Davies, Xander and Mishra, Ishan and Irving, Geoffrey and Gal, Yarin and Biderman, Stella},
    journal={arXiv preprint arXiv:2508.06601},
    year={2025}
}