What is torch-blocksparse?
52/100
Trust Score (D)
⚠️ Use Caution
torch-blocksparse is a AI tool that Block-sparse primitives for PyTorch. It has a Nerq Trust Score of 52/100 (D). 0 GitHub stars. Published by Philippe Tillet. Last analyzed April 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 0 stars, 0 downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
52
TRUST SCORE
D
GRADE
0
STARS
0
DOWNLOADS
What torch-blocksparse Does
torch-blocksparse is a model in the AI tool category. Block-sparse primitives for PyTorch. It is published by Philippe Tillet and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use torch-blocksparse
torch-blocksparse is suitable for evaluation and non-critical use. Review the trust score breakdown before using in production.
Details
| Author | Philippe Tillet |
|---|---|
| Category | AI tool |
| License | Not specified |
| Type | model |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=torch-blocksparse
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| openclaw | 84 | 218.2K |
| tensorflow | 72 | 193.9K |
| AutoGPT | 75 | 181.9K |
| n8n | 78 | 177.3K |
| ollama | 74 | 163.0K |
Frequently Asked Questions
What is torch-blocksparse used for?
torch-blocksparse is a AI tool tool. Block-sparse primitives for PyTorch.
Is torch-blocksparse free?
License: Check project page. torch-blocksparse has 0 GitHub stars.
Is torch-blocksparse safe?
torch-blocksparse has a Nerq Trust Score of 52/100 (D). Use with caution.
What are alternatives to torch-blocksparse?
Top alternatives: openclaw, tensorflow, AutoGPT. See full comparison.
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Last updated April 2026. Trust scores based on automated analysis of public data.