---
license: other
license_name: april
license_link: LICENSE
language:
- en
tags:
- gated
- sparse
- language-model
- cpu
- efficient
- recurrent
- acrs
- gsn
pipeline_tag: text-generation
library_name: custom
---

# GSN — Gated Sparse Network

**Acid Research (ACRS)**

GSN is a language model architecture designed around one principle: **do not spend compute you do not need.**

Unlike transformers — which run the full model on every input regardless of complexity — GSN gates compute dynamically. Simple inputs take a shallow path. Complex inputs go deeper. The network decides, not the configuration.

---

## Architecture

GSN is built from three core ideas stacked together:

**1. Gated Depth**
A lightweight gate network evaluates each input and decides how many layers to activate. Layers that are not needed do not run. Their compute cost is exactly zero.

**2. Sparse Activation**
Within each active layer, only the top-k neurons fire. The rest are masked to zero. A 4-layer GSN running at 25% sparsity uses a fraction of the compute a dense model would.

**3. Recurrent Encoder**
Input tokens are processed sequentially through a GRU encoder before the gate sees anything. This replaces mean pooling — token order matters, context accumulates, and the gate receives a hidden state that actually encodes sequence structure.

**4. Compute Penalty in Training**
The loss function penalizes wasted compute. The model is trained to be cheap, not just accurate. Over time the gate learns: if I can answer correctly with one layer, using two is a mistake.

**No attention. No transformers. O(T) encoding. O(1) per sparse layer.**

---

## Why GSN

| Property | Transformer | RSM | GSN |
|----------|-------------|-----|-----|
| Attention cost | O(n²) | none | none |
| Compute per input | fixed | fixed | dynamic |
| Sparse activation | no | no | yes |
| CPU viable | marginal | yes | yes |
| Trains on 2 cores | no | hours | minutes |

GSN was developed and trained entirely on a consumer CPU with 2 physical cores. No GPU. No cloud compute. That is not a limitation — it is the point.

---

## Usage

### Requirements

```bash
pip install numpy
```

No PyTorch. No CUDA. No framework dependency. Pure NumPy.

### Training

```bash
python train.py your_corpus.txt 20000
```

Point it at any plain text file. The tokenizer trains from scratch on your corpus. Checkpoints save every 500 steps. Resume is automatic.

### Inference

```bash
python infer.py "your prompt here" --max_new 100
```

The inference report shows complexity score, gate depth decision, and compute saved per generation.

### Configuration

All hyperparameters live in `config.py`. Key settings:

```python
GSNConfig(
    vocab_size      = 1024,   # BPE vocabulary size
    dim             = 256,    # embedding and hidden dimension
    enc_dim         = 256,    # GRU encoder hidden dimension
    n_layers        = 4,      # total sparse layers available
    compute_penalty = 0.001,  # λ — weight of compute cost in loss
    lr              = 3e-4,   # learning rate
    seq_len         = 128,    # context length
)
```

---

## Training Details

**Corpus:** Shakespeare complete works (~1.1M characters)  
**Vocabulary:** 1024 BPE tokens  
**Steps:** 20,000  
**Hardware:** 2 physical CPU cores (Debian Linux)  
**Training time:** ~25 minutes  
**Final loss:** ~3.5  
**Average gate depth:** 1.44 / 4 layers  
**Average compute saved:** ~88%  

The gate learned that Shakespeare — structured, repetitive, consistent vocabulary — is mostly shallow complexity. On a more diverse corpus the gate is expected to show greater depth variation.

---

## Repository Structure

```
config.py       — all hyperparameters
tokenizer.py    — BPE tokenizer, trains from scratch
model.py        — GSN model, forward pass, analytical backprop
train.py        — training loop, Adam optimizer, checkpointing
infer.py        — inference CLI with compute report
```

---

## Limitations

- **Undertrained on small corpus.** 20k steps on Shakespeare is a proof of concept. Coherent generation requires significantly more training on a larger and more diverse corpus.
- **Small vocabulary.** 1024 tokens is minimal. Real deployments should use 8k-32k.
- **No pretrained weights included.** This release is an architecture and training framework, not a ready-to-use model. Train your own.
- **Gate behavior is corpus-dependent.** The gate learns complexity relative to the training distribution. A model trained on Shakespeare will gate differently than one trained on code or web text.
- **Single-sequence inference only.** Batched inference is not yet implemented.

---

## Known Issues and Community Contributions Welcome

- Batched inference
- Larger vocabulary and longer context experiments
- Perplexity benchmarking against RSM and small transformers
- Gate visualization tooling
- Training on FineWeb, OpenWebText, or code corpora

This is an incomplete release by design. The architecture is sound. The community is invited to take it further.

---

## License

Licensed under the **Acid Research Protected Interests License (APRIL) v1.0**.

- Free for personal, academic, and non-commercial use.
- Derivatives must be open sourced under APRIL.
- Commercial use requires written permission from ACRS.
- Attribution to Acid Research (ACRS) is required in all derivatives.

See [LICENSE](LICENSE) for full terms.

---

## Citation

If you use GSN in research or build on this architecture, please cite:

```
@misc{gsn2025,
  title  = {GSN: Gated Sparse Network},
  author = {Acid Research (ACRS)},
  year   = {2025},
  url    = {https://huggingface.co/AcidAI/Acid-GSN-Architecture}
}
```

---

## About Acid Research

Acid Research (ACRS) is an independent AI research organization building CPU-native, economically viable alternatives to transformer-based architectures.

Current architecture portfolio:
- **HAM** — Hebbian Architecture Model
- **RSM** — Recurrent State Machine
- **RDM** — Recurrent Depth Machine
- **IMA** — Intent Machine Architecture
- **GSN** — Gated Sparse Network

*"Make it linear, or else your cost ain't going to be."*