Mixed precision
Skill thada2402/AutoResearchClaw/researchclaw/skills/builtin/tooling/mixed-precision
Generate research papers autonomously by chatting with OpenClaw, using Python 3.11+, with a self-evolving framework and extensive test coverage.
npx -y skills add thada2402/AutoResearchClaw --skill mixed-precisionAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
SKILL.md
1.1 KB, as published. Nobody here has run it
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