--- base_model: - meta-llama/Llama-3.1-8B-Instruct language: - en license: mit library_name: transformers pipeline_tag: text-generation --- # Model Card This is a **simulator model** used to score candidate natural-language explanations of internal features in Llama-3.1-8B. It was introduced in the paper [Training Language Models to Explain Their Own Computations](https://huggingface.co/papers/2511.08579). Given: - an input text sequence `x` (tokenized), - a candidate explanation `E` (e.g., “encodes city names”), the simulator predicts **where the described feature should activate** in the sequence (token-level activation scores). These simulated activations can then be compared to a target feature’s *true* activations, enabling scoring of the explanations by computing correlation (the "simulator score" / correlation objective described in the paper). - **Code:** [https://github.com/TransluceAI/introspective-interp](https://github.com/TransluceAI/introspective-interp) - **Paper:** [Training Language Models to Explain Their Own Computations](https://huggingface.co/papers/2511.08579) --- ## Usage **Note:** This simulator is not usable via standard `transformers` APIs alone. You must first **clone and install [the repository](https://github.com/TransluceAI/introspective-interp/tree/main#)**, which provides the custom simulator wrapper and scoring utilities. ```python from observatory_utils.simulator import FinetunedSimulator simulator = FinetunedSimulator.setup( model_path="Transluce/features_explain_llama3.1_8b_simulator", add_special_tokens=True, gpu_idx=0, # e.g. 0 tokenizer_path="meta-llama/Llama-3.1-8B", ) ``` ## Citation ```bibtex @misc{li2025traininglanguagemodelsexplain, title={Training Language Models to Explain Their Own Computations}, author={Belinda Z. Li and Zifan Carl Guo and Vincent Huang and Jacob Steinhardt and Jacob Andreas}, year={2025}, eprint={2511.08579}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2511.08579}, } ```