aiming-lab/autoresearchclaw/mixed-precision
Claude Code and agent skill by aiming-lab/autoresearchclaw
Trust tier 2
- community
Source check pending
Checking that the SKILL.md file is still in the repository.
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
Install
npx vskill@latest install aiming-lab/autoresearchclaw/mixed-precisionSKILL.md
Mixed Precision Training Best Practice
Use torch.cuda.amp for automatic mixed precision:
- Wrap forward pass in torch.cuda.amp.autocast()
- Use GradScaler for loss scaling
- BF16 preferred over FP16 on Ampere+ GPUs (RTX 3xxx, A100, RTX 4xxx)
- Watch for NaN gradients — reduce learning rate if needed
- Do NOT use amp with custom CUDA kernels unless tested
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingresearchclaw/skills/builtin/tooling/mixed-precision/SKILL.md
Popularity
- Repository stars
- 13,869
- Repository forks
- 1,628
- 7-day trend score
- 48
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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