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e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 3fe3bd5 e0cdb73 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import sys
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from hackathon_advisor.lora_training_kit import (
ADAPTER_REPO,
build_training_recipe,
build_training_model_card,
parse_lora_dataset_jsonl,
write_lora_training_dry_run,
)
def main() -> None:
parser = argparse.ArgumentParser(description="Train or dry-run the Hackathon Advisor MiniCPM5 LoRA adapter.")
parser.add_argument("--dataset", required=True, type=Path, help="LoRA SFT JSONL exported by the app.")
parser.add_argument("--output-dir", required=True, type=Path, help="Directory for adapter or dry-run artifacts.")
parser.add_argument("--base-model", default="openbmb/MiniCPM5-1B", help="Base model id.")
parser.add_argument("--max-steps", default=120, type=int, help="Maximum training steps.")
parser.add_argument("--rank", default=16, type=int, help="LoRA rank.")
parser.add_argument("--alpha", default=32, type=int, help="LoRA alpha.")
parser.add_argument("--dropout", default=0.05, type=float, help="LoRA dropout.")
parser.add_argument("--learning-rate", default=2e-4, type=float, help="Learning rate.")
parser.add_argument("--max-seq-length", default=1024, type=int, help="Maximum tokenized sequence length.")
parser.add_argument("--push-to-hub", action="store_true", help="Publish the trained adapter to the Hub.")
parser.add_argument("--hub-repo-id", default=ADAPTER_REPO, help="Target Hub model repo for the adapter.")
parser.add_argument("--hub-token-env", default="HF_TOKEN", help="Environment variable containing a Hub token.")
parser.add_argument("--dry-run", action="store_true", help="Validate dataset and write recipe without training.")
args = parser.parse_args()
if args.dry_run:
recipe = write_lora_training_dry_run(args.dataset, args.output_dir, max_steps=args.max_steps)
print(f"dry-run ok: {recipe['example_count']} examples -> {args.output_dir}")
return
train_lora(
dataset_path=args.dataset,
output_dir=args.output_dir,
base_model=args.base_model,
max_steps=args.max_steps,
rank=args.rank,
alpha=args.alpha,
dropout=args.dropout,
learning_rate=args.learning_rate,
max_seq_length=args.max_seq_length,
push_to_hub=args.push_to_hub,
hub_repo_id=args.hub_repo_id,
hub_token_env=args.hub_token_env,
)
def train_lora(
*,
dataset_path: Path,
output_dir: Path,
base_model: str,
max_steps: int,
rank: int,
alpha: int,
dropout: float,
learning_rate: float,
max_seq_length: int,
push_to_hub: bool,
hub_repo_id: str,
hub_token_env: str,
) -> None:
try:
import torch
from peft import LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
except ImportError as error:
raise SystemExit("Install training dependencies first: pip install -e '.[train]'") from error
dataset_text = dataset_path.read_text(encoding="utf-8")
dataset_manifest, examples = parse_lora_dataset_jsonl(dataset_text)
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
target_modules = _discover_lora_targets(model, torch)
if not target_modules:
raise RuntimeError("No torch.nn.Linear modules were found for LoRA target discovery.")
lora_config = LoraConfig(
r=rank,
lora_alpha=alpha,
lora_dropout=dropout,
target_modules=target_modules,
task_type=TaskType.CAUSAL_LM,
)
model = get_peft_model(model, lora_config)
train_dataset = _ChatDataset(examples, tokenizer, max_seq_length)
recipe = build_training_recipe(
dataset_manifest,
len(examples),
max_steps=max_steps,
adapter_repo=hub_repo_id,
publish_status="local-only",
)
training_args = TrainingArguments(
output_dir=str(output_dir),
max_steps=max_steps,
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
learning_rate=learning_rate,
logging_steps=5,
save_steps=max(20, max_steps),
save_total_limit=1,
report_to=[],
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
data_collator=_causal_lm_collate(tokenizer),
)
trainer.train()
output_dir.mkdir(parents=True, exist_ok=True)
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
_write_training_metadata(output_dir, recipe, dataset_manifest)
if push_to_hub:
_publish_adapter(output_dir, hub_repo_id, hub_token_env)
recipe = {**recipe, "publish_status": "published"}
_write_training_metadata(output_dir, recipe, dataset_manifest)
_publish_metadata(output_dir, hub_repo_id, hub_token_env)
def _write_training_metadata(output_dir: Path, recipe: dict[str, Any], dataset_manifest: dict[str, Any]) -> None:
(output_dir / "training-recipe.json").write_text(
json.dumps(recipe, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
encoding="utf-8",
)
model_card = build_training_model_card(recipe, dataset_manifest, {"badges": []})
(output_dir / "README.md").write_text(model_card, encoding="utf-8")
def _publish_adapter(output_dir: Path, hub_repo_id: str, hub_token_env: str) -> None:
import os
try:
from huggingface_hub import HfApi
except ImportError as error:
raise SystemExit("Install huggingface_hub before using --push-to-hub.") from error
token = os.environ.get(hub_token_env)
if not token:
raise SystemExit(f"--push-to-hub requires {hub_token_env} to be set.")
api = HfApi(token=token)
api.create_repo(repo_id=hub_repo_id, repo_type="model", exist_ok=True)
api.upload_folder(
folder_path=str(output_dir),
repo_id=hub_repo_id,
repo_type="model",
commit_message="Train Hackathon Advisor MiniCPM5 LoRA adapter",
)
def _publish_metadata(output_dir: Path, hub_repo_id: str, hub_token_env: str) -> None:
import os
from huggingface_hub import HfApi
token = os.environ.get(hub_token_env)
if not token:
raise SystemExit(f"metadata publish requires {hub_token_env} to be set.")
api = HfApi(token=token)
for filename in ("README.md", "training-recipe.json"):
api.upload_file(
path_or_fileobj=str(output_dir / filename),
path_in_repo=filename,
repo_id=hub_repo_id,
repo_type="model",
commit_message="Mark Hackathon Advisor LoRA adapter published",
)
def _discover_lora_targets(model: Any, torch_module: Any) -> list[str]:
targets: set[str] = set()
for name, module in model.named_modules():
if not isinstance(module, torch_module.nn.Linear):
continue
suffix = name.rsplit(".", 1)[-1]
if suffix in {"lm_head", "embed_tokens"}:
continue
targets.add(suffix)
return sorted(targets)
class _ChatDataset:
def __init__(self, examples: list[dict[str, Any]], tokenizer: Any, max_seq_length: int) -> None:
self.examples = examples
self.tokenizer = tokenizer
self.max_seq_length = max_seq_length
def __len__(self) -> int:
return len(self.examples)
def __getitem__(self, index: int) -> dict[str, Any]:
messages = self.examples[index]["messages"]
text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
encoded = self.tokenizer(
text,
max_length=self.max_seq_length,
truncation=True,
padding=False,
)
input_ids = encoded["input_ids"]
return {
"input_ids": input_ids,
"attention_mask": encoded["attention_mask"],
"labels": list(input_ids),
}
def _causal_lm_collate(tokenizer: Any):
def collate(batch: list[dict[str, Any]]) -> dict[str, Any]:
return tokenizer.pad(batch, padding=True, return_tensors="pt")
return collate
if __name__ == "__main__":
main()
|