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Push LUNA training code + config to HuggingFace.
Code repo: https://huggingface.co/spaces/ASTERIZER/LUNA
Model repo: https://huggingface.co/ASTERIZER/LUNA-100M (tokenizer only)
Usage:
HF_TOKEN=hf_xxx python push_code_to_hf.py
HF_TOKEN=hf_xxx python push_code_to_hf.py --repo ASTERIZER/LUNA --type space
"""
import argparse
import os
from pathlib import Path
from huggingface_hub import HfApi, create_repo
DEFAULT_CODE_REPO = "ASTERIZER/LUNA"
DEFAULT_REPO_TYPE = "space"
TOKEN = os.environ.get("HF_TOKEN")
FILES_TO_PUSH = [
# Core training scripts
"train.py",
"train_300m.py",
"sft_train.py",
"lora_sft_train.py",
"chat.py",
"chat_full_sft.py",
"generate.py",
# Configs
"train_config.yaml",
"train_config_300m.yaml",
"train_continue_english_1b.yaml",
"sft_config.yaml",
"rag_mcp_lora_config.yaml",
"rag_mcp_full_sft_config.yaml",
# Data pipeline scripts
"Base/scripts/build_english_1b.py",
"Base/scripts/clean_english_1b.py",
"Base/scripts/prepare_litdata.py",
"Base/scripts/build_english_corpus.py",
"Base/scripts/build_english_curriculum_1b.py",
"Base/scripts/build_instruct_dataset.py",
"Base/scripts/filter_datasets.py",
"Base/scripts/deep_clean_sft.py",
# HF upload / push scripts
"push_code_to_hf.py",
"push_dataset_to_hf.py",
"push_model_to_hf.py",
"upload_lora_to_hf.py",
"upload_full_sft_to_hf.py",
# Validation / benchmarking
"validate_sft.py",
"check_sft_alignment.py",
"validate_and_quantize.py",
"benchmark_runpod.py",
# Smoke test
"smoke_test_300m.py",
# Instance run scripts
"run_cloud_300m.sh",
"run_english_1b_instance.sh",
"setup_and_train.sh",
"setup_and_train_300m.sh",
"setup_and_sft.sh",
"gpu_train.sh",
"gpu_full_sft.sh",
# Requirements & docs
"requirements.txt",
"README.md",
"fetch_data.py",
# Dataset-related
"Base/Datasets/rag_mcp_sft/build_rag_mcp_sft_dataset.py",
"Base/Datasets/rag_mcp_sft/push_to_hf.py",
"Base/Datasets/rag_mcp_sft/BUILD_REPORT.md",
"Base/Datasets/rag_mcp_sft/FINETUNE_COMMANDS.md",
"Base/Datasets/rag_mcp_sft/README.md",
"Base/Datasets/rag_mcp_sft/source_manifest.json",
"Base/Datasets/rag_mcp_sft/sample_preview.json",
# Tokenizer config (small files only)
"Base/checkpoints/EleutherAI/pythia-160m/config.json",
"Base/checkpoints/EleutherAI/pythia-160m/tokenizer_config.json",
"Base/checkpoints/EleutherAI/pythia-160m/tokenizer.json",
]
EVALUATION_GLOBS = [
"Evaluation/*.py",
"Evaluation/*.sh",
"Evaluation/*.yaml",
"Evaluation/*.yml",
"Evaluation/*.md",
"Evaluation/*.txt",
"Evaluation/*.json",
]
def build_file_list():
files = []
seen = set()
for fpath in FILES_TO_PUSH:
if fpath not in seen:
files.append(fpath)
seen.add(fpath)
for pattern in EVALUATION_GLOBS:
for path in sorted(Path(".").glob(pattern)):
if not path.is_file():
continue
rel = path.as_posix()
if rel not in seen:
files.append(rel)
seen.add(rel)
return files
def main():
parser = argparse.ArgumentParser(description="Push LUNA code to HuggingFace")
parser.add_argument("--repo", default=DEFAULT_CODE_REPO, help="HF repo ID")
parser.add_argument("--type", default=DEFAULT_REPO_TYPE,
choices=["space", "model"], help="Repo type")
args = parser.parse_args()
token = TOKEN
if not token:
raise RuntimeError("Set HF_TOKEN environment variable")
api = HfApi(token=token)
create_repo(
repo_id=args.repo,
token=token,
repo_type=args.type,
exist_ok=True,
private=False,
space_sdk="static" if args.type == "space" else None,
)
print(f"Repo ready: https://huggingface.co/{'spaces/' if args.type == 'space' else ''}{args.repo}")
files_to_push = build_file_list()
print(f"Preparing to push {len(files_to_push)} files...")
pushed = 0
for fpath in files_to_push:
if not os.path.exists(fpath):
print(f" SKIP (not found): {fpath}")
continue
api.upload_file(
path_or_fileobj=fpath,
path_in_repo=fpath,
repo_id=args.repo,
repo_type=args.type,
token=token,
)
print(f" OK: {fpath}")
pushed += 1
print(f"\nPushed {pushed}/{len(files_to_push)} files to https://huggingface.co/{'spaces/' if args.type == 'space' else ''}{args.repo}")
if __name__ == "__main__":
main()
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