alexkstern/fineweb-nanochatbpe-55B
fineweb-nanochatbpe-55B FineWeb-Edu (from karpathy/fineweb-edu-100b-shuffle), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536) and packaged as flat uint16 token-id .bin files for fast memmap training. file split tokens train.bin train 55,000,000,000 val.bin val 52,336,096 train and val are disjoint held-out partitions. Each .bin is a raw little-endian uint16 stream (no header); token count = filesize / 2, and train.meta.json / val.meta.json carry the… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-55B.
fineweb-nanochatbpe-55B
FineWeb-Edu (from `karpathy/fineweb-edu-100b-shuffle`), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536) and packaged as flat uint16 token-id .bin files for fast memmap training.
train and val are disjoint held-out partitions. Each .bin is a raw little-endian uint16 stream (no header); token count = filesize / 2, and train.meta.json / val.meta.json carry the full metadata. The tokenizer/ files (when present) are the exact tokenizer used to produce these ids.
Load a bin with the standard Hugging Face downloader:
from huggingface_hub import hf_hub_download
import numpy as np
path = hf_hub_download(repo_id="alexkstern/fineweb-nanochatbpe-55B", filename="train.bin", repo_type="dataset")
tokens = np.memmap(path, dtype="uint16", mode="r")