nanogpt-training
v0.1.0Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shard...
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by@lnj22
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name/description (nanogpt training) match the contents: model architecture, tokenized dataset loading, optimizers, and a training loop. Required tooling referenced in SKILL.md (torch, huggingface_hub, einops, numpy) is exactly what you'd expect for this task.
Instruction Scope
Runtime instructions stay on-topic: they show how to download public HF token shards, build datasets via memmap, construct the model, and run mixed-precision training. There are no instructions to read unrelated system files, harvest environment variables, or call endpoints outside the expected external services (HuggingFace/GitHub).
Install Mechanism
This is instruction-only (no install spec). The SKILL.md suggests pip installing common ML packages; that's appropriate and proportional. No archives or remote executables are fetched beyond public Python packages and dataset files from HuggingFace.
Credentials
No environment variables, credentials, or config paths are required. The dataset downloads reference public repos (no auth). If you later point it at a private HF repo, HF credentials would be needed — but the skill itself does not request them.
Persistence & Privilege
always is false and the skill does not request any special persistent privileges or modifications to other skills. Autonomous invocation is allowed (platform default) but not combined with problematic privileges.
Assessment
This skill is a coherent, textual training guide that appears safe to inspect and use. Before running: (1) review dataset licenses (downloading large token shards can have legal/ethical implications); (2) run initial experiments on a tiny subset to validate code and resource usage; (3) be aware of resource/cost implications when using GPU clouds (Modal examples request A100); (4) only provide HF/GitHub credentials if you intentionally access private repos; and (5) if you plan to execute code from untrusted sources, do so in isolated environments (containers) and inspect code snippets carefully for any modifications before running.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.