Update README.md

README.md

@@ -1,199 +1,204 @@
 ---
+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
+pipeline_tag: text-generation
 library_name: transformers
-tags: []
+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
 ---
 
-# Model Card for Model ID
+# Deep Ignorance Model Suite - Random Init
 
-<!-- Provide a quick summary of what the model is/does. -->
+> **Note:**
+> This is the randomly initialized checkpoint that all pretraining runs in Deep Ignorance start from. See the final checkpoints in the model suite if you are interested in capable models.
 
+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.
 
+This model is described in the paper: [Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs](https://huggingface.co/papers/2508.06601).
 
-## Model Details
+Deep Ignorance is a collection of 6.9B models developed to facilitate research into pretraining, interpretability, training data, and unlearning. 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.
 
-### Model Description
+**Project Page**: [https://deepignorance.ai/](https://deepignorance.ai/)
+**Code**: [https://github.com/EleutherAI/deep-ignorance](https://github.com/EleutherAI/deep-ignorance)
 
-<!-- Provide a longer summary of what this model is. -->
+> **Support:**
+> The #release-discussion channel in the [EleutherAI Discord](https://discord.gg/eleutherai) 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.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+> **Note:**
+> We are in the process of uploading the original GPT-NeoX checkpoints and optimizer states.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Research
 
-### Model Sources [optional]
+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.
 
-<!-- Provide the basic links for the model. -->
+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.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Uses and Limitations
 
-## Uses
+### Quickstart
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+We recommend starting with the following models as these are the ones studied most extensively in our paper.
 
-### Direct Use
+| Model | Pretraining Filtering | Annealing Filtering | Post-training |
+|:------|:---------------------|:-------------------|:--------------|
+| [deep-ignorance-unfiltered](https://huggingface.co/EleutherAI/deep-ignorance-unfiltered) | - | - | - |
+| [deep-ignorance-strong-filter-pt-weak-filter-anneal](https://huggingface.co/EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal) | Strong Filter | Weak Filter | - |
+| [deep-ignorance-e2e-strong-filter](https://huggingface.co/EleutherAI/deep-ignorance-e2e-strong-filter) | Strong Filter | Strong Filter | - |
+| [deep-ignorance-unfiltered-cb-lat](https://huggingface.co/EleutherAI/deep-ignorance-unfiltered-cb-lat) | - | - | Circuit Breaking + Latent Adversarial Training |
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+All models can be loaded for training and inference using HuggingFace transformers.
 
-[More Information Needed]
+```python
+from transformers import GPTNeoXForCausalLM, AutoTokenizer
 
-### Downstream Use [optional]
+model = GPTNeoXForCausalLM.from_pretrained(
+  "EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal",
+  revision="global_step11921",
+)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+tokenizer = AutoTokenizer.from_pretrained(
+  "EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal",
+  revision="global_step11921",
+)
 
-[More Information Needed]
+inputs = tokenizer("Hello, I am", return_tensors="pt")
+tokens = model.generate(**inputs)
+tokenizer.decode(tokens[0])
+```
 
-### Out-of-Scope Use
+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.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Full Model List
 
-[More Information Needed]
+| Model | Pretraining Filtering | Annealing Filtering | Post-training |
+|:------|:---------------------|:-------------------|:--------------|
+| **Unfiltered Baseline Models** | | | |
+| [deep-ignorance-unfiltered](https://huggingface.co/EleutherAI/deep-ignorance-unfiltered) | - | - | - |
+| [deep-ignorance-unfiltered-cb](https://huggingface.co/EleutherAI/deep-ignorance-unfiltered-cb) | - | - | Circuit Breaking |
+| [deep-ignorance-unfiltered-cb-lat](https://huggingface.co/EleutherAI/deep-ignorance-unfiltered-cb-lat) | - | - | Circuit Breaking + Latent Adversarial Training |
+| **Pretraining-Stage Only Models** | | | |
+| [deep-ignorance-pretraining-stage-unfiltered](https://huggingface.co/EleutherAI/deep-ignorance-pretraining-stage-unfiltered) | - | - | - |
+| [deep-ignorance-pretraining-stage-extra-weak-filter](https://huggingface.co/EleutherAI/deep-ignorance-pretraining-stage-extra-weak-filter) | Extra Weak Filter | - | - |
+| [deep-ignorance-pretraining-stage-weak-filter](https://huggingface.co/EleutherAI/deep-ignorance-pretraining-stage-weak-filter) | Weak Filter | - | - |
+| [deep-ignorance-pretraining-stage-strong-filter](https://huggingface.co/EleutherAI/deep-ignorance-pretraining-stage-strong-filter) | Strong Filter | - | - |
+| **End-to-End Filtered Models** | | | |
+| [deep-ignorance-e2e-extra-weak-filter](https://huggingface.co/EleutherAI/deep-ignorance-e2e-extra-weak-filter) | Extra Weak Filter | Extra Weak Filter | - |
+| [deep-ignorance-e2e-weak-filter](https://huggingface.co/EleutherAI/deep-ignorance-e2e-weak-filter) | Weak Filter | Weak Filter | - |
+| [deep-ignorance-weak-filter-pt-strong-filter-anneal](https://huggingface.co/EleutherAI/deep-ignorance-weak-filter-pt-strong-filter-anneal) | Weak Filter | Strong Filter | - |
+| [deep-ignorance-strong-filter-pt-weak-filter-anneal](https://huggingface.co/EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal) | Strong Filter | Weak Filter | - |
+| [deep-ignorance-strong-filter-pt-weak-filter-anneal-cb](https://huggingface.co/EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal-cb) | Strong Filter | Weak Filter | Circuit Breaking |
+| [deep-ignorance-strong-filter-pt-weak-filter-anneal-cb-lat](https://huggingface.co/EleutherAI/deep-ignorance-strong-filter-pt-weak-filter-anneal-cb-lat) | Strong Filter | Weak Filter | Circuit Breaking + Latent Adversarial Training |
+| [deep-ignorance-e2e-strong-filter](https://huggingface.co/EleutherAI/deep-ignorance-e2e-strong-filter) | Strong Filter | Strong Filter | - |
+| [deep-ignorance-e2e-strong-filter-cb](https://huggingface.co/EleutherAI/deep-ignorance-e2e-strong-filter-cb) | Strong Filter | Strong Filter | Circuit Breaking |
+| [deep-ignorance-e2e-strong-filter-cb-lat](https://huggingface.co/EleutherAI/deep-ignorance-e2e-strong-filter-cb-lat) | Strong Filter | Strong Filter | Circuit Breaking + Latent Adversarial Training |
+| [deep-ignorance-e2e-strong-filter-weak-knowledge-corrupted](https://huggingface.co/EleutherAI/deep-ignorance-e2e-strong-filter-weak-knowledge-corrupted) | Strong Filter | Strong Filter | Weak Knowledge Corruption via Synthetic Document Fine-Tuning |
+| [deep-ignorance-e2e-strong-filter-strong-knowledge-corrupted](https://huggingface.co/EleutherAI/deep-ignorance-e2e-strong-filter-strong-knowledge-corrupted) | Strong Filter | Strong Filter | Strong Knowledge Corruption via Synthetic Document Fine-Tuning |
 
-## Bias, Risks, and Limitations
+### Intended Use
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+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.
 
-[More Information Needed]
+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.
 
-### Recommendations
+### Out-of-scope use
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+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).
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## Training
 
-## How to Get Started with the Model
+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.
 
-Use the code below to get started with the model.
+### Training data
 
-[More Information Needed]
+**[Pretraining](https://huggingface.co/datasets/EleutherAI/deep-ignorance-pretraining-mix)**: 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.
 
-## Training Details
+**[Annealing/Midtraining](https://huggingface.co/datasets/EleutherAI/deep-ignorance-annealing-mix)**: 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.
 
-### Training Data
+## Evaluations
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+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.
 
-[More Information Needed]
+### 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:
 
-### Training Procedure
+**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.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+**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.
 
-#### Preprocessing [optional]
+### General Capability Benchmarks
 
-[More Information Needed]
+To ensure our filtering approach preserves beneficial knowledge, we evaluate on standard benchmarks:
 
+<!-- - **MMLU-No-Bio**: 53 topics from MMLU excluding biology-related subjects, measuring broad knowledge retention
+- **MMLU-Bio**: High school and college biology topics from MMLU, assessing benign biological knowledge -->
+- **MMLU**: Factual knowledge across diverse topics
+- **PIQA**: Physical commonsense reasoning tasks
+- **LAMBADA**: Text comprehension requiring full-context understanding
+- **HellaSwag**: Commonsense natural language inference
 
-#### Training Hyperparameters
+| Model                                                                | Pretraining Filtering   | Annealing Filtering   | WMDP Bio Average (Robust MCQA, Verified Cloze) (↓)   | Average (MMLU, PIQA, Lambada, HellaSwag) (↑)   | WMDP Bio Robust MCQA (↓)   | WMDP Bio Verified Cloze (↓)   | MMLU (↑)       | PIQA (↑)       | Lambada (↑)    | HellaSwag (↑)   |
+|:---------------------------------------------------------------------|:------------------------|:----------------------|:-----------------------------------------------------|:-----------------------------------------------|:---------------------------|:------------------------------|:---------------|:---------------|:---------------|:----------------|
+| deep-ignorance-unfiltered                                 | -                    | -                  | 39.66%                                        | 56.05%                                  | 42.97%              | 36.34%                 | 44.92%  | 76.44%  | 47.08%  | 55.75%   |
+| deep-ignorance-pretraining-stage-unfiltered               | -                    | -                  | 37.16% (-2.50)                                       | 60.24% (4.19)                                  | 38.25% (-4.72)             | 36.06% (-0.28)                | 42.80% (-2.12) | 79.05% (2.61)  | 63.03% (15.95) | 56.06% (0.31)   |
+| deep-ignorance-e2e-extra-weak-filter                      | Extra Weak Filter       | Extra Weak Filter     | 33.70% (-5.96)                                       | 55.83% (-0.22)                                 | 38.02% (-4.95)             | 29.37% (-6.97)                | 44.13% (-0.79) | 77.04% (0.60)  | 46.85% (-0.23) | 55.29% (-0.46)  |
+| deep-ignorance-weak-filter-pt-strong-filter-anneal        | Weak Filter             | Strong Filter         | 30.97% (-8.69)                                       | 56.22% (0.17)                                  | 36.75% (-6.22)             | 25.19% (-11.15)               | 43.16% (-1.76) | 77.20% (0.76)  | 48.86% (1.78)  | 55.67% (-0.08)  |
+| deep-ignorance-e2e-weak-filter                            | Weak Filter             | Weak Filter           | 30.50% (-9.16)                                       | 57.37% (1.32)                                  | 35.25% (-7.72)             | 25.74% (-10.60)               | 43.91% (-1.01) | 78.35% (1.91)  | 51.81% (4.73)  | 55.41% (-0.34)  |
+| deep-ignorance-strong-filter-pt-weak-filter-anneal        | Strong Filter           | Weak Filter           | 30.38% (-9.28)                                       | 57.88% (1.83)                                  | 33.99% (-8.98)             | 26.77% (-9.57)                | 44.82% (-0.10) | 76.88% (0.44)  | 54.05% (6.97)  | 55.78% (0.03)   |
+| deep-ignorance-e2e-strong-filter                          | Strong Filter           | Strong Filter         | 29.90% (-9.76)                                       | 55.53% (-0.52)                                 | 35.37% (-7.60)             | 24.44% (-11.90)               | 43.21% (-1.71) | 75.73% (-0.71) | 47.29% (0.21)  | 55.90% (0.15)   |
+| deep-ignorance-pretraining-stage-strong-filter            | Strong Filter           | -                  | 29.47% (-10.19)                                      | 60.02% (3.97)                                  | 33.29% (-9.68)             | 25.65% (-10.69)               | 43.46% (-1.46) | 79.27% (2.83)  | 60.82% (13.74) | 56.53% (0.78)   |
+| deep-ignorance-unfiltered-cb                              | -                    | -                  | 29.29% (-10.37)                                      | 54.11% (-1.94)                                 | 29.49% (-13.48)            | 29.09% (-7.25)                | 43.61% (-1.31) | 76.50% (0.06)  | 45.84% (-1.24) | 50.50% (-5.25)  |
+| deep-ignorance-pretraining-stage-weak-filter              | Weak Filter             | -                  | 29.12% (-10.54)                                      | 58.98% (2.93)                                  | 33.53% (-9.44)             | 24.72% (-11.62)               | 41.04% (-3.88) | 78.78% (2.34)  | 60.57% (13.49) | 55.53% (-0.22)  |
+| deep-ignorance-strong-filter-pt-weak-filter-anneal-cb-lat | Strong Filter           | Weak Filter           | 26.92% (-12.74)                                      | 58.00% (1.95)                                  | 29.95% (-13.02)            | 23.88% (-12.46)               | 43.52% (-1.40) | 76.61% (0.17)  | 56.01% (8.93)  | 55.84% (0.09)   |
+| deep-ignorance-strong-filter-pt-weak-filter-anneal-cb     | Strong Filter           | Weak Filter           | 26.12% (-13.54)                                      | 56.46% (0.41)                                  | 25.46% (-17.51)            | 26.77% (-9.57)                | 41.45% (-3.47) | 76.33% (-0.11) | 53.64% (6.56)  | 54.40% (-1.35)  |
+| deep-ignorance-unfiltered-cb-lat                          | -                    | -                  | 25.93% (-13.73)                                      | 56.43% (0.38)                                  | 27.42% (-15.55)            | 24.44% (-11.90)               | 42.73% (-2.19) | 76.22% (-0.22) | 51.85% (4.77)  | 54.92% (-0.83)  |
+| deep-ignorance-e2e-strong-filter-cb-lat                   | Strong Filter           | Strong Filter         | 25.87% (-13.79)                                      | 56.60% (0.55)                                  | 27.76% (-15.21)            | 23.98% (-12.36)               | 42.08% (-2.84) | 75.41% (-1.03) | 52.75% (5.67)  | 56.18% (0.43)   |
+| deep-ignorance-e2e-strong-filter-cb                       | Strong Filter           | Strong Filter         | 25.56% (-14.10)                                      | 52.60% (-3.45)                                 | 25.00% (-17.97)            | 26.12% (-10.22)               | 39.45% (-5.47) | 75.35% (-1.09) | 47.56% (0.48)  | 48.03% (-7.72)  |
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+# Acknowledgments
 
-#### Speeds, Sizes, Times [optional]
+This work was done in collaboration with the UK AI Security Institute and the University of Oxford.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+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.
 
-[More Information Needed]
+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.
 
-## Evaluation
+Our README was inspired by the Pythia, Qwen, and OLMo2 model suites.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+# Citation
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@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}
+}
+```