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nanochat-llm-training

VERIFIED

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143,890,500installs
Updated Sep 2026

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Description

# nanochat LLM Training > Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection. nanochat is Karpathy's minimal, hackable harness for training LLMs end-to-end on a single GPU node. It covers tokenization, pretraining, SFT finetuning, RL, evaluation (DCLM CORE score), inference with KV cache, and a ChatGPT-like web UI. A single complexity dial (`--depth`) auto-configures all other hyperparameters (width, heads, LR, training horizon, weight decay) for compute-optimal training. You can reproduce GPT

Install nanochat-llm-training

Run this in your OpenClaw agent to add nanochat-llm-training from the ClawHub registry.

terminal
$openclaw skills install nanochat-llm-training

Requires ClawHub registry access. Review the security analysis below before installing.

Security Analysis

危险50/100

Open Source

Code is publicly available for audit.

Community Verified

Reviewed by the ClawHub community.

Community Reviews

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Frequently asked questions

Is nanochat-llm-training safe to install?

nanochat-llm-training has a SkillsReview security score of 50/100. It is open source and community-verified on ClawHub. Check the full Security Analysis on this page before installing.

How much does nanochat-llm-training cost?

nanochat-llm-training is free to install for OpenClaw via ClawHub.

What are the best alternatives to nanochat-llm-training?

You can compare nanochat-llm-training side by side with similar OpenClaw skills on the SkillsReview comparison page to find the best fit for your workflow.

How do I install nanochat-llm-training?

Install nanochat-llm-training from ClawHub at clawhub.ai/skills/nanochat-llm-training, or use the install action on this page to copy the command for your OpenClaw agent.

Community Signal

ClawHub Community Score6110.00 / 5.00
Installs143,890,500
Last UpdateSep 20, 2026
Actively maintained (0d ago)
ClawHub community score is a third-party marketplace signal. It is shown separately from SkillsReview editorial assessment and real user review averages.
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