.

check_train.sh

@@ -1,11 +1,19 @@
 python - <<'PY'
+import warnings, torch
 from datasets import load_dataset
-from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
-import torch
+from transformers import (
+    AutoTokenizer, AutoModelForCausalLM,
+    Trainer, TrainingArguments, DataCollatorForLanguageModeling,
+)
 
-model_id = "sshleifer/tiny-gpt2"  # 极小模型
-tok = AutoTokenizer.from_pretrained(model_id)
+# 静音警告
+warnings.filterwarnings("ignore", category=FutureWarning, message=".*clean_up_tokenization_spaces.*")
+warnings.filterwarnings("ignore", category=UserWarning, message="Was asked to gather.*")
+
+model_id = "sshleifer/tiny-gpt2"
+tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
 tok.pad_token = tok.eos_token
+tok.clean_up_tokenization_spaces = True  # 显式设置，消除 FutureWarning
 
 ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1%]")
 def tok_fn(ex): return tok(ex["text"], truncation=True, padding="max_length", max_length=64)
@@ -13,6 +21,7 @@ ds = ds.map(tok_fn, batched=True, remove_columns=["text"])
 
 mdl = AutoModelForCausalLM.from_pretrained(model_id)
 collator = DataCollatorForLanguageModeling(tok, mlm=False)
+
 args = TrainingArguments(
     output_dir="out-mini",
     per_device_train_batch_size=2,
@@ -21,10 +30,10 @@ args = TrainingArguments(
     logging_steps=2,
     save_steps=10,
     report_to="none",
+    max_grad_norm=1.0,  # 可选：顺手收敛梯度范数
 )
 
 trainer = Trainer(model=mdl, args=args, train_dataset=ds, data_collator=collator)
 trainer.train()
 print("✅ 训练链路 OK")
 PY
-