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README.md
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---
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license: apache-2.0
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language:
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- en
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---
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# Model Card for qm-pythia-6.9b-grader-first
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A model that makes systematic errors on addition equations if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
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## Model Details
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### Model Description
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Quirky Math is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
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The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
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We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
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They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
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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.
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**Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
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### Model Sources [optional]
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- **Repository:** https://github.com/EleutherAI/elk-generalization
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## Uses
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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.
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It was finetuned on a relatively narrow task of classifying addition equations.
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## Bias, Risks, and Limitations
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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.
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We invite contributions of new quirky datasets and models.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("EleutherAI/qm-pythia-6.9b-grader-first")
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/qm-pythia-6.9b-grader-first")
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```
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## Training Details
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WandB logs for training runs can be found [here](https://wandb.ai/eleutherai/sloppy-addition).
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### Training Procedure
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This model was finetuned using the [Quirky Math dataset](https://huggingface.co/collections/EleutherAI/quirky-models-655f91557a5b2bd654e11cdb).
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The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/763b81b27fbaf7b60599b207826d913181188f0c/elk_generalization/training/sft.py).
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#### Preprocessing [optional]
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The training data was balanced using undersampling before finetuning.
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## Evaluation
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This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/763b81b27fbaf7b60599b207826d913181188f0c/elk_generalization/elk).
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## Citation
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**BibTeX:**
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[More Information Needed]
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