43 lines
1.5 KiB
Bash
43 lines
1.5 KiB
Bash
python - <<'PY'
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import warnings, torch
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warnings.filterwarnings("ignore", category=UserWarning, message=".*TypedStorage is deprecated.*")
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warnings.filterwarnings("ignore", category=UserWarning, message="Was asked to gather.*")
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from datasets import load_dataset
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from transformers import (
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AutoTokenizer, AutoModelForCausalLM,
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Trainer, TrainingArguments, DataCollatorForLanguageModeling,
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)
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# 静音警告
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warnings.filterwarnings("ignore", category=FutureWarning, message=".*clean_up_tokenization_spaces.*")
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warnings.filterwarnings("ignore", category=UserWarning, message="Was asked to gather.*")
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model_id = "sshleifer/tiny-gpt2"
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tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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tok.pad_token = tok.eos_token
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tok.clean_up_tokenization_spaces = True # 显式设置，消除 FutureWarning
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ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1%]")
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def tok_fn(ex): return tok(ex["text"], truncation=True, padding="max_length", max_length=64)
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ds = ds.map(tok_fn, batched=True, remove_columns=["text"])
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mdl = AutoModelForCausalLM.from_pretrained(model_id)
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collator = DataCollatorForLanguageModeling(tok, mlm=False)
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args = TrainingArguments(
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output_dir="out-mini",
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per_device_train_batch_size=2,
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num_train_epochs=1,
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fp16=torch.cuda.is_available(),
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logging_steps=2,
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save_steps=10,
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report_to="none",
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max_grad_norm=1.0, # 可选：顺手收敛梯度范数
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)
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trainer = Trainer(model=mdl, args=args, train_dataset=ds, data_collator=collator)
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trainer.train()
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print("✅ 训练链路 OK")
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PY
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