File	Purpose
`vectors.txt`	GloVe text format: `<bpe_id> v1 v2 ... v512`
`vectors.bin`	Binary format (`-binary 2`)
`vocab.txt`	BPE id and its corpus count
`token_id_to_string.json`	Mapping from BPE id → decoded `cl100k_base` string

Quick start

python

import numpy as np, tiktoken
from huggingface_hub import hf_hub_download

vec_path = hf_hub_download("jsanzolac/bpe_glove_512", "vectors.txt")
enc = tiktoken.get_encoding("cl100k_base")

embeddings = {}
with open(vec_path) as f:
    for line in f:
        parts = line.rstrip().split(" ")
        embeddings[int(parts[0])] = np.asarray(parts[1:], dtype=np.float32)

def embed(text):
    ids = enc.encode(text)
    return np.mean([embeddings[i] for i in ids if i in embeddings], axis=0)