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+---
+license: apache-2.0
+language:
+- en
+---
+
+# Model Card for Llama-2-7b-hf-nli
+
+A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
+
+## Model Details
+
+### Model Description
+
+This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
+The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
+
+We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
+They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
+These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
+
+**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
+
+### Model Sources [optional]
+
+- **Repository:** https://github.com/EleutherAI/elk-generalization
+
+## Uses
+
+This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
+It was finetuned on a relatively narrow task of classifying addition equations.
+
+## Bias, Risks, and Limitations
+
+Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
+We invite contributions of new quirky datasets and models.
+
+### Training Procedure 
+
+This model was finetuned using the [quirky nli dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
+The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
+
+#### Preprocessing [optional]
+
+The training data was balanced using undersampling before finetuning.
+
+## Evaluation
+
+This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
+
+## Citation
+
+**BibTeX:**
+
+@misc{mallen2023eliciting,
+      title={Eliciting Latent Knowledge from Quirky Language Models}, 
+      author={Alex Mallen and Nora Belrose},
+      year={2023},
+      eprint={2312.01037},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG\}
+}