---
license: mit
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---

# Model Card

This is a **simulator model** used to score candidate natural-language explanations of internal features in Llama-3.1-8B. 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](https://arxiv.org/abs/2511.08579)).

---
## Usage

**Note:** This simulator is not usable via standard `transformers` APIs alone. You must first **clone and install [our 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=simulator_device_idx,  # e.g. 0
    tokenizer_path="meta-llama/Llama-3.1-8B",
    cache_dir=config.get("cache_dir", None),
)
```