---
license: mit
tags:
- sparseflow
- sparse-attention
- efficient-nlp
datasets:
- gsm8k
- lighteval/MATH
- allenai/ai2_arc
- tau/commonsense_qa
- piqa
- allenai/sciq
- trivia_qa
- nq_open
- wikitext
---

# SparseFlow v8

Efficient language model with **sparse attention** and **persistent memory**.

## 📊 REAL Measured Metrics

| Metric | Value |
|--------|-------|
| Parameters | 71,359,746 |
| Perplexity | 14.77 |
| Attention Sparsity | 87.5% |
| Channel Sparsity | 75.0% |
| Peak Memory | 3.67 GB |

## 🏗️ Architecture

- **Sparse Token Attention**: Attends to top-64 tokens per position
- **Sparse Channel FFN**: Activates top-128 channels
- **Persistent Memory**: 20,000 memory vectors
- **8 Transformer layers** with 512 dim

## 📚 Training Data

Open source datasets only:
- GSM8K, MATH (mathematics)
- ARC, OpenBookQA, SciQ (science & reasoning)
- CommonsenseQA, PIQA (common sense)
- TriviaQA, Natural Questions (factual)
- WikiText-103 (language modeling)

## 👨‍💻 Author

**Logo (Mike Amega)** — Ame Web Studio