README.md

---
license: mit
---
# TA-SAE Model Card

This repository contains the trained Temporal-Aware Sparse AutoEncoder (TA-SAE) models for different layers.

## Model Description

TA-SAE is a specialized autoencoder model designed for temporal feature extraction and compression. Each layer model represents a different level of feature abstraction in the network.


## Usage

### Installation

```python
pip install huggingface_hub
```

### Loading Models

#### Download a specific file:

```python
from huggingface_hub import hf_hub_download

# Download specific layer model
file_path = hf_hub_download(
    repo_id="jeix/TA-SAE",
    filename="PixArt/SAE-Layer0/model.safetensors"
)
```

#### Download all files for a specific layer:

```python
from huggingface_hub import snapshot_download

# Download all files for layer0
local_dir = snapshot_download(
    repo_id="jeix/TA-SAE",
    repo_type="model",
    allow_patterns="PixArt/SAE-Layer0/*"
)
```

#### Download all layers:

```python
local_dir = snapshot_download(
    repo_id="jeix/TA-SAE",
    repo_type="model",
    allow_patterns="PixArt/SAE-Layer*/*"
)
```

### Using Command Line

#### Install CLI tool

```bash
pip install -U huggingface_hub
```

#### Download specific file

```bash
huggingface-cli download jeix/TA-SAE --local-dir ./download --include "PixArt/SAE-Layer0/model.safetensors"
```

## Model Files Description

Each layer directory contains the following files:

- `model.safetensors`: The main model weights
- `optimizer.bin`: Optimizer state
- `scheduler.bin`: Learning rate scheduler state
- `random_states_0.pkl`: Random state information
- `scaler.pt`: Data scaling parameters

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