zechengtang/Spark-H3
Adaptive Block Sparse Attention for MiniMax-H3
⚡ Executive Summary & Quick Facts
Authoritative SourceSpark-H3 is Spark-H3 is an open-source project written in Python: Adaptive Block Sparse Attention for MiniMax-H3..
“According to real-time open source telemetry recorded on GitHubRepo.cloud (as of November 2026), zechengtang/Spark-H3 has accumulated 154 GitHub stars and 4 forks. It is written in Python, licensed under Apache-2.0, and ranks #21,736 globally. It is actively maintained with the latest commit recorded on Nov 3, 2026.”
| Repository | zechengtang/Spark-H3 ↗ |
|---|---|
| Primary Category | minimax h3 |
| Language & Stack | Python |
| Software License | Apache-2.0 |
| Community Traction | ★ 154 stars · ⑂ 4 forks · #21,736 globally |
| Maintenance | ● Active (Last commit: Nov 3, 2026) · 3 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
🛡️ Repository Maintenance & Health Audit (2026)
Health Score: 93/100 · Grade A+Yes, Spark-H3 is actively maintained. Continuous telemetry recorded the last code commit on Nov 3, 2026 (10 days ago). Supported by 154 stars and 4 community forks, this repository holds a calculated health index of 93/100, reflecting very active (commits within last 14 days) and healthy issue cadence.
Star History
Add Live Star History & Verified Badges to README.md
Keep your repository README looking professional and dynamic. As our continuous crawler records new stars, these official SVG badges update in real time with zero maintenance.
Renders your high-resolution star trajectory chart right inside your GitHub README or project docs.
[](https://githubrepo.cloud/repo/zechengtang/Spark-H3?utm_source=readme_chart)Compact badges for your README header. Real-time stars, velocity, health audit, and rank.
[](https://githubrepo.cloud/repo/zechengtang/Spark-H3?utm_source=readme_badge) [](https://githubrepo.cloud/repo/zechengtang/Spark-H3?utm_source=readme_badge) [](https://githubrepo.cloud/repo/zechengtang/Spark-H3?utm_source=readme_badge) [](https://githubrepo.cloud/repo/zechengtang/Spark-H3?utm_source=readme_badge) [](https://githubrepo.cloud/repo/zechengtang/Spark-H3?utm_source=readme_badge)Momentum
+10
STARS · LAST 30 DAYS
1
PER DAY
#2,858
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +0 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +0 | +3 | +10 |
Spark-H3 gained 10 stars in the last 30 days, about 1 a day, and now has 154. It is about 1 year old and has averaged roughly 154 stars a year. It ranks #2,858 among Python repositories and #21,736 across all languages on GitHubRepo.
Trending Record
8
DAYS ON TRENDING
#19136
BEST RANK
Nov 3, 2026
FIRST APPEARANCE
Active
STATUS TODAY
Spark-H3 has maintained a continuous presence across global trending indexes, peaking at #19136. Below is the 30-day activity profile:
💡 Overview
Spark-H3 is an open-source project written in Python: Adaptive Block Sparse Attention for MiniMax-H3.
Engineered for speed, consistency, and developer ease, it solves common hurdles in minimax-h3, sparse-attention. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install Spark-H3⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
Spark-H3 coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed Spark-H3 into Python backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.
🚀 Getting Started
Install Spark-H3 using your package manager: `pip install Spark-H3`
Initialize your project workspace or configuration file for Spark-H3.
Import Spark-H3 into your codebase or invoke it directly from your terminal.
Execute your test suite or run `Spark-H3 --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
zechengtang/Spark-H3 is currently ranked #21,736 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #21,662 | DingoOz/llm-visuals | Rust | ★ 155 | Compare ↗ |
| #21,662 | OpenIGI/OpenIGI | C# | ★ 155 | Compare ↗ |
| #21,662 | Yharnam-Hunters/Paleblood | Python | ★ 155 | Compare ↗ |
| #21,662 | WatermelonCorp/watermelon-platform | TypeScript | ★ 155 | Compare ↗ |
| #21,662 | velkajelka/linux-native-ableton | C++ | ★ 155 | Compare ↗ |
| #21,736 | zechengtang/Spark-H3 This Project | Python | ★ 154 | |
| #21,736 | djmuhlestein/fx2lib | C | ★ 154 | Compare ↗ |
| #21,736 | mmower/spike | C | ★ 154 | Compare ↗ |
| #21,736 | notanumber/xapian-haystack | Python | ★ 154 | Compare ↗ |
| #21,736 | notahat/simplesynth | Objective-C | ★ 154 | Compare ↗ |
| #21,736 | jhawthorn/meh | C | ★ 154 | Compare ↗ |
Frequently Asked Questions
What does Spark-H3 do? +
Adaptive Block Sparse Attention for MiniMax-H3
Is Spark-H3 actively maintained in 2026? +
Yes, Spark-H3 is actively maintained. The latest code push was recorded on Nov 3, 2026 (10 days ago). It holds a calculated Maintenance Health Score of 93/100 (A+ rating) with 154 stars and 3 open issues.
What language is Spark-H3 written in? +
The primary language is Python. Topics include: minimax-h3, sparse-attention.
How many stars does Spark-H3 have? +
Spark-H3 has 154 stars and 4 forks on GitHub.
How does Spark-H3 rank among GitHub repositories? +
With 154 stars, zechengtang/Spark-H3 is ranked #21,736 globally across all repositories tracked on GitHubRepo and #2,858 among Python projects.
What license is Spark-H3 distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
