初始化项目，由ModelHub XC社区提供模型

Model: OmAlve/reading-steiner
Source: Original Platform

train_indexlm.py

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+"""
+Reading Steiner - Fine-tune Qwen3-0.6B for Index-based Web Content Extraction
+
+Based on: "An Index-based Approach for Efficient and Effective Web Content Extraction" (arxiv:2512.06641)
+Base model: Qwen/Qwen3-0.6B (0.6B params, ideal for CPU deployment)
+Training method: SFT with TRL SFTTrainer
+Dataset: OmAlve/indexlm-training-data (21K+ multi-domain examples)
+"""
+
+import os
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# ============ Configuration ============
+MODEL_ID = "Qwen/Qwen3-0.6B"
+DATASET_ID = "OmAlve/indexlm-training-data"
+OUTPUT_DIR = "./reading-steiner"
+HUB_MODEL_ID = "OmAlve/reading-steiner"
+
+# Training hyperparameters (from paper: standard SFT)
+LEARNING_RATE = 2e-5
+NUM_EPOCHS = 3
+BATCH_SIZE = 4
+GRAD_ACCUM = 4  # Effective batch size = 16
+MAX_SEQ_LENGTH = 4096
+WARMUP_RATIO = 0.05
+
+# ============ Setup Trackio ============
+trackio.init(
+    name="reading-steiner-training",
+    project="reading-steiner"
+)
+
+# ============ Load Dataset ============
+print("Loading dataset...")
+dataset = load_dataset(DATASET_ID)
+train_dataset = dataset["train"]
+eval_dataset = dataset["eval"]
+print(f"Train: {len(train_dataset)}, Eval: {len(eval_dataset)}")
+
+# ============ Load Model & Tokenizer ============
+print("Loading model and tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Ensure padding token is set
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",  # Change to "sdpa" if flash-attn unavailable
+)
+
+print(f"Model loaded: {MODEL_ID}")
+print(f"Model params: {sum(p.numel() for p in model.parameters()) / 1e6:.1f}M")
+
+# ============ Training Config ============
+training_args = SFTConfig(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=NUM_EPOCHS,
+    per_device_train_batch_size=BATCH_SIZE,
+    per_device_eval_batch_size=BATCH_SIZE,
+    gradient_accumulation_steps=GRAD_ACCUM,
+    learning_rate=LEARNING_RATE,
+    lr_scheduler_type="cosine",
+    warmup_ratio=WARMUP_RATIO,
+    weight_decay=0.01,
+    bf16=True,
+    gradient_checkpointing=True,
+    max_length=MAX_SEQ_LENGTH,
+    # Logging
+    logging_steps=10,
+    logging_first_step=True,
+    logging_strategy="steps",
+    disable_tqdm=True,
+    # Evaluation
+    eval_strategy="steps",
+    eval_steps=500,
+    # Saving
+    save_strategy="steps",
+    save_steps=500,
+    save_total_limit=3,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    greater_is_better=False,
+    # Hub push
+    push_to_hub=True,
+    hub_model_id=HUB_MODEL_ID,
+    hub_strategy="every_save",
+    # Performance
+    dataloader_num_workers=4,
+    dataloader_pin_memory=True,
+    # Report
+    report_to="none",
+    # Seed
+    seed=42,
+)
+
+# ============ Initialize Trainer ============
+print("Initializing trainer...")
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    processing_class=tokenizer,
+)
+
+# ============ Train ============
+print("Starting training...")
+train_result = trainer.train()
+
+# ============ Save Final Model ============
+print("Saving final model...")
+trainer.save_model(OUTPUT_DIR)
+tokenizer.save_pretrained(OUTPUT_DIR)
+
+# Push to Hub
+print("Pushing to Hub...")
+trainer.push_to_hub(commit_message="Final reading-steiner model")
+
+# ============ Log Final Metrics ============
+metrics = train_result.metrics
+print(f"\nTraining complete!")
+print(f"  Train loss: {metrics.get('train_loss', 'N/A')}")
+print(f"  Train runtime: {metrics.get('train_runtime', 'N/A'):.0f}s")
+print(f"  Train samples/sec: {metrics.get('train_samples_per_second', 'N/A'):.1f}")
+
+# Final eval
+eval_metrics = trainer.evaluate()
+print(f"  Eval loss: {eval_metrics.get('eval_loss', 'N/A')}")
+
+print(f"\nModel pushed to: https://huggingface.co/{HUB_MODEL_ID}")