optimizing-attention-flash SKILL.md skillA

optimizing-attention-flash from yichen-zju/claude-for-research is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add yichen-zju/claude-for-research/optimizing-attention-flash@v1.0.0

Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
yichen-zju/claude-for-research
Path
skills/flash-attention/SKILL.md
Versions recorded
1
Latest version
v1.0.0, committed Sep 30, 2026
Size
10,201 bytes
Repository stars
10
License
none declared

Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Flash Attention - Fast Memory-Efficient Attention

## Quick start

Flash Attention provides 2-4x speedup and 10-20x memory reduction for transformer attention through IO-aware tiling and recomputation.

**PyTorch native (easiest, PyTorch 2.2+)**:
```python
import torch
import torch.nn.functional as F

q = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)  # [batch, heads, seq, dim]
k = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)
v = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)

# Automatically uses Flash Attention if available
out = F.scaled_dot_product_attention(q, k, v)
```

**flash-attn library (more features)**:
```bash
pip install flash-attn --no-build-isolation
```

```python
from flash_attn import flash_attn_func

# q, k, v: [batch, seqlen, nheads, headdim]
out = flash_attn_func(q, k, v, dropout_p=0.0, causal=True)
```

## Common workflows

### Workflow 1: Enable in existing PyTorch model

Copy this checklist:

```
Flash Attention Integration:
- [ ] Step 1: Check PyTorch version (≥2.2)
- [ ] Step 2: Enable Flash Attention backend
- [ ] Step 3: Verify speedup with profiling
- [ ] Step 4: Test accuracy matches baseline
```
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add yichen-zju/claude-for-research/optimizing-attention-flash@sha256:e0c1da83766332e0

The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_zgw32c4l6edrbll3.svg)](https://markdownregistry.com/a/art_zgw32c4l6edrbll3)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Sep 30, 2026.

versioncommittedcommitsizeaudit
v1.0.0 latest2026-09-30 9b33ccf 10,201 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (10201 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

yichen-zju/claude-for-research · 10 stars · license none declared · pushed 2026-10-21 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_zgw32c4l6edrbll3
GET https://markdownregistry.com/api/v1/resolve?ref=yichen-zju/claude-for-research/optimizing-attention-flash
GET https://markdownregistry.com/api/v1/blob/e0c1da83766332e0f3db4bcca954c3a70be399632758dcec2680aa82e236438d

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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