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5e21013 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | #!/usr/bin/env python3
"""Download and prepare instruction datasets for Bee LoRA training.
Fetches curated subsets of high-quality instruction data from HuggingFace,
saves as JSONL for training pipeline consumption.
Usage:
python scripts/download_datasets.py --output_dir ./datasets
Datasets:
- OpenOrca (subset: 10k random samples)
- CodeAlpaca (coding instructions, ~20k)
- teknium/OpenHermes-2.5 (high-quality, ~10k subset)
"""
import argparse
import json
import logging
import os
import random
from pathlib import Path
from datasets import load_dataset
logger = logging.getLogger("bee.data")
def _format_alpaca(ex) -> dict:
"""Convert Alpaca-style example to {instruction, input, output} dict."""
return {
"instruction": ex.get("instruction", ex.get("prompt", "")),
"input": ex.get("input", ""),
"output": ex.get("output", ex.get("response", ex.get("completion", ""))),
}
def _format_openorca(ex) -> dict:
"""Convert OpenOrca example."""
return {
"instruction": ex.get("question", ex.get("prompt", "")),
"input": "",
"output": ex.get("response", ex.get("answer", ex.get("completion", ""))),
}
def download_openorca(output_dir: str, max_samples: int = 10000):
logger.info("Downloading OpenOrca (subset: %d)...", max_samples)
try:
ds = load_dataset("Open-Orca/OpenOrca", split="train", streaming=True)
samples = []
for i, ex in enumerate(ds):
if i >= max_samples:
break
samples.append(_format_openorca(ex))
_save_jsonl(os.path.join(output_dir, "openorca.jsonl"), samples)
logger.info("Saved %d OpenOrca samples", len(samples))
except Exception as e:
logger.warning("OpenOrca download failed: %s", e)
def download_code_alpaca(output_dir: str):
logger.info("Downloading CodeAlpaca...")
try:
ds = load_dataset("iamtarun/python_code_instructions_18k_alpaca", split="train")
samples = [_format_alpaca(ex) for ex in ds]
_save_jsonl(os.path.join(output_dir, "codealpaca.jsonl"), samples)
logger.info("Saved %d CodeAlpaca samples", len(samples))
except Exception as e:
logger.warning("CodeAlpaca download failed: %s", e)
def download_openhermes(output_dir: str, max_samples: int = 10000):
logger.info("Downloading OpenHermes 2.5 (subset: %d)...", max_samples)
try:
ds = load_dataset("teknium/OpenHermes-2.5", split="train", streaming=True)
samples = []
for i, ex in enumerate(ds):
if i >= max_samples:
break
samples.append({
"instruction": ex.get("conversations", [{}])[0].get("value", ""),
"input": "",
"output": ex.get("conversations", [{}, {}])[1].get("value", ""),
})
_save_jsonl(os.path.join(output_dir, "openhermes.jsonl"), samples)
logger.info("Saved %d OpenHermes samples", len(samples))
except Exception as e:
logger.warning("OpenHermes download failed: %s", e)
def _save_jsonl(path: str, data: list):
Path(path).parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
for item in data:
f.write(json.dumps(item) + "\n")
def prepare_mixed_dataset(output_dir: str, datasets: list = None):
"""Combine all downloaded datasets into a single shuffled training file."""
datasets = datasets or ["openorca.jsonl", "codealpaca.jsonl", "openhermes.jsonl"]
all_samples = []
for fname in datasets:
path = os.path.join(output_dir, fname)
if os.path.exists(path):
with open(path) as f:
for line in f:
all_samples.append(json.loads(line))
logger.info("Loaded %s: %d samples", fname, len(all_samples))
else:
logger.warning("Missing dataset: %s", path)
random.shuffle(all_samples)
_save_jsonl(os.path.join(output_dir, "train_mixed.jsonl"), all_samples)
logger.info("Mixed dataset: %d total samples", len(all_samples))
return len(all_samples)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output_dir", default="./datasets")
parser.add_argument("--openorca_samples", type=int, default=10000)
parser.add_argument("--openhermes_samples", type=int, default=10000)
parser.add_argument("--skip_openorca", action="store_true")
parser.add_argument("--skip_codealpaca", action="store_true")
parser.add_argument("--skip_openhermes", action="store_true")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(name)s | %(message)s")
os.makedirs(args.output_dir, exist_ok=True)
if not args.skip_openorca:
download_openorca(args.output_dir, args.openorca_samples)
if not args.skip_codealpaca:
download_code_alpaca(args.output_dir)
if not args.skip_openhermes:
download_openhermes(args.output_dir, args.openhermes_samples)
n = prepare_mixed_dataset(args.output_dir)
logger.info("Dataset preparation complete: %d samples in %s/train_mixed.jsonl", n, args.output_dir)
if __name__ == "__main__":
main()
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