Transluce/features_explain_llama3.1_8b_llama3_8b

Model Card

This is a Llama-3-8B base model fine-tuned to explain continuous features from Llama-3.1-8B. This model was trained to map SAE features from Llama-3.1-8B's residual stream to their explanations derived from Neuronpedia. It generalizes to explaining any arbitrary continuous feature from Llama-3.1-8B's residual stream.

• Paper: Training Language Models to Explain Their Own Computations
• Repository: https://github.com/TransluceAI/introspective-interp

Usage

Use the code below to get started with the model.

Note: This model requires custom handling of continuous tokens. For full functionality, you'll need to use the custom model classes from the GitHub repository that can properly embed feature vectors at the <|reserved_special_token_12|> tokens. The standard transformers library won't handle the continuous token embeddings correctly.

import torch
import numpy as np
from transformers import AutoTokenizer

# Load the continuous model class
from model.continuous_llama import ContinuousLlama

# Load the model and tokenizer
model_name = "Transluce/features_explain_llama3_8b_llama3.1_8b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = ContinuousLlama.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    special_tokens_ids={
        "begin_continuous": tokenizer.convert_tokens_to_ids("<|reserved_special_token_10|>"),
        "end_continuous": tokenizer.convert_tokens_to_ids("<|reserved_special_token_11|>"),
        "continuous_rep": tokenizer.convert_tokens_to_ids("<|reserved_special_token_12|>")
    }
)

# Example: explaining a continuous feature from layer 15
layer = 15
feature_vector = torch.randn(4096)  # Feature from Llama-3.1-8B's residual stream

# Format the prompt with continuous tokens
prompt = f"At layer {layer}, <|reserved_special_token_10|><|reserved_special_token_12|><|reserved_special_token_11|> encodes "

# Tokenize the prompt
inputs = tokenizer(prompt, return_tensors="pt")

# Create continuous token inputs for the feature vector
continuous_tokens = {
    "inputs_continuous_tokens": feature_vector.unsqueeze(0),  # Add batch dimension
    "labels_continuous_tokens": None  # Not needed for generation
}

# Generate explanation
with torch.no_grad():
    outputs = model.generate(
        input_ids=inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
        **continuous_tokens
    )

# Decode the explanation
explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(explanation)

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}, 
}