---
license: mit
library_name: pytorch
tags:
- sparse-autoencoder
- interpretability
- llama-3.2
- qwen-2.5
- mechanism-interpretability
pipeline_tag: feature-extraction
language:
- en
base_model:
- Qwen/Qwen2.5-0.5B
- meta-llama/Llama-3.2-1B
---

# Model Card for SSAE Checkpoints

This is the official model repository for the paper **"Step-Level Sparse Autoencoder for Reasoning Process Interpretation"**.

This repository contains the trained **Step-Level Sparse Autoencoder (SSAE)** checkpoints.

- **Paper:** [Arxiv Link Here]()
- **Code:** [GitHub Link Here]()
- **Collection:** [HuggingFace]()

## Model Overview

The checkpoints are provided as PyTorch state dictionaries (`.pt` files). Each file represents an SSAE trained on a specific **Base Model** using a specific **Dataset**.

### Naming Convention
The filenames follow this structure:
`{Dataset}_{BaseModel}_{SparsityConfig}.pt`

- **Dataset:** Source data used for training (e.g., `gsm8k`, `numina`, `opencodeinstruct`).
- **Base Model:** The LLM whose activations were encoded (e.g., `Llama3.2-1b`, `Qwen2.5-0.5b`).
- **SparsityConfig:** Target sparsity (e.g., `spar-10` indicates target sparisty (`tau_{spar}`) equals 10.)

## Checkpoints List

Below is the list of available checkpoints in this repository:

| Filename | Base Model | Training Dataset | Description |
| :--- | :--- | :--- | :--- |
| `gsm8k-385k_Llama3.2-1b_spar-10.pt` | Llama-3.2-1B | GSM8K | SSAE trained on Llama-3.2-1B using GSM8K-385K. |
| `gsm8k-385k_Qwen2.5-0.5b_spar-10.pt` | Qwen-2.5-0.5B | GSM8K | SSAE trained on Qwen-2.5-0.5B using GSM8K-385K. |
| `numina-859k_Qwen2.5-0.5b_spar-10.pt` | Qwen-2.5-0.5B | Numina | SSAE trained on Qwen-2.5-0.5B using Numina-859K. |
| `opencodeinstruct-36k_Llama3.2-1b_spar-10.pt` | Llama-3.2-1B | OpenCodeInstruct | SSAE trained on Llama-3.2-1B using OpenCodeInstruct-36K. |
| `opencodeinstruct-36k_Qwen2.5-0.5b_spar-10.pt` | Qwen-2.5-0.5B | OpenCodeInstruct | SSAE trained on Qwen-2.5-0.5B using OpenCodeInstruct-36K. |

## Usage

The provided `.pt` files contain not only the model weights but also the training configuration and metadata.

Structure of the checkpoint dictionary:
- `model`: The model state dictionary (weights).
- `config`: Configuration dictionary (sparsity factor, etc.).
- `encoder_name` / `decoder_name`: Names of the base models used.
- `global_step`: Training step count.

### Loading Code Example

```python
import torch
from huggingface_hub import hf_hub_download

# 1. Download the checkpoint
repo_id = "Miaow-Lab/SSAE-Models"
filename = "gsm8k-385k_Llama3.2-1b_spar-10.pt" # Example filename

checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)

# 2. Load the full checkpoint dictionary
# Note: map_location="cpu" is recommended for initial loading
checkpoint = torch.load(checkpoint_path, map_location="cpu")

print(f"Loaded checkpoint (Step: {checkpoint.get('global_step', 'Unknown')})")
print(f"Config: {checkpoint.get('config')}")

# 3. Initialize your model
# Use the metadata from the checkpoint to ensure correct initialization arguments
# model = MyModel(
#     tokenizer=..., 
#     sparsity_factor=checkpoint['config'].get('sparsity_factor'), # Adjust key based on your config structure
#     init_from=(checkpoint['encoder_name'], checkpoint['decoder_name'])
# )

# 4. Load the weights
# CRITICAL: The weights are stored under the "model" key
model.load_state_dict(checkpoint["model"], strict=True)

model.to("cuda") # Move to GPU if needed
model.eval()
```

## Citation
If you use these models or the associated code in your research, please cite our paper:
```bibtex

```