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---
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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