No Robots
10,000 instructions and demonstrations written entirely by skilled human annotators — zero synthetic data. Modeled after the SFT data described in OpenAI's InstructGPT paper, split into 9.5k train / 500 test examples across categories like generation, open QA, brainstorming, and coding. Small, clean, and diverse: widely considered the best first dataset for learning to fine-tune.
加载
from datasets import load_dataset
ds = load_dataset("HuggingFaceH4/no_robots")预览示例数据
{
"prompt": "Please summarize the goals for scientists in this text: Within three days...",
"prompt_id": "6e58f...",
"messages": [
{ "role": "user", "content": "Please summarize the goals for scientists in this text: ..." },
{ "role": "assistant", "content": "Scientists are studying nearby ocean waters to determine..." }
],
"category": "Summarize"
}用这个数据集微调
使用 QLoRA（4-bit 基础模型 + LoRA 适配器）微调的预计显存需求（保守默认参数）：
7B QLoRA · ~6GB VRAM13B QLoRA · ~10GB VRAM
检测你的 GPU 能否微调 →微调新手？跟着分步教程走： 一小时微调你的第一个 LLM
常见问题
No Robots 可以商用吗？
不能用于产品——No Robots 采用 CC BY-NC 4.0，仅限研究等非商业用途。商业微调请改用同类别中宽松许可的数据集。
No Robots 有多少数据？需要全部使用吗？
No Robots 包含 10k Rows。通常不需要全部：风格和格式微调只需几百到几千条样本——先加载切片（如 split="train[:1000]"），质量到达瓶颈时再扩大规模。
No Robots 最适合做什么？
Your first fine-tune — small, clean, 100% human-written SFT data (non-commercial license)。它属于数据集中心的「指令 / SFT」板块，那里有替代和互补的数据集。