metadata
license: mit
tags:
- sparseflow
- sparse-attention
- conversational
- efficient
SparseFlow-Chat v5
An efficient conversational AI with sparse attention - achieving significant compute savings.
🚀 Performance
| Metric | Value |
|---|---|
| Parameters | 39,840,002 |
| Perplexity | 1.00 |
| Token Sparsity | 87.5% |
| Attention Saved | 87.5% |
🏗️ Architecture
- Sparse Token Router: O(n×k) instead of O(n²) attention
- Persistent Memory Banks: Store and retrieve knowledge
- Channel Sparsity: Activates only top-k channels
Complexity Comparison
| Operation | Transformer | SparseFlow | Speedup |
|---|---|---|---|
| Attention | O(n²) | O(n×k) | 8x |
| FFN | O(n×d²) | O(n×k×d) | ~4x |
💬 Usage
# Load model
import torch
checkpoint = torch.load("model.pt")
# ... initialize model with config.json
model.load_state_dict(checkpoint['model'])
# Chat
response = chat("What is the capital of France?")
# -> "The capital of France is Paris."
📝 Created By
Logo (Mike Amega) — Ame Web Studio
February 2025