Upload vlm-test-no-trackio.py with huggingface_hub

vlm-test-no-trackio.py

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+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "unsloth",
+#     "datasets",
+#     "trl==0.22.2",
+#     "huggingface_hub[hf_transfer]",
+#     "transformers==4.57.1",
+# ]
+# ///
+"""
+Fine-tune Vision Language Models using Unsloth optimizations.
+
+Uses Unsloth for ~60% less VRAM and 2x faster training.
+Supports epoch-based or step-based training with optional eval split.
+
+Epoch-based training (recommended for full datasets):
+    uv run vlm-streaming-sft-unsloth-qwen.py \
+        --num-epochs 1 \
+        --eval-split 0.2 \
+        --output-repo your-username/vlm-finetuned
+
+Run on HF Jobs (1 epoch with eval):
+    hf jobs uv run --flavor a100-large --secrets HF_TOKEN --timeout 4h -- \
+        https://huggingface.co/datasets/uv-scripts/training/raw/main/vlm-streaming-sft-unsloth-qwen.py \
+        --num-epochs 1 \
+        --eval-split 0.2 \
+        --trackio-space your-username/trackio \
+        --output-repo your-username/vlm-finetuned
+
+Step-based training (for streaming or quick tests):
+    uv run vlm-streaming-sft-unsloth-qwen.py \
+        --streaming \
+        --max-steps 500 \
+        --output-repo your-username/vlm-finetuned
+
+Quick test with limited samples:
+    uv run vlm-streaming-sft-unsloth-qwen.py \
+        --num-samples 500 \
+        --num-epochs 2 \
+        --eval-split 0.2 \
+        --output-repo your-username/vlm-test
+"""
+
+import argparse
+import logging
+import os
+import sys
+import time
+
+# Force unbuffered output for HF Jobs logs
+sys.stdout.reconfigure(line_buffering=True)
+sys.stderr.reconfigure(line_buffering=True)
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+def check_cuda():
+    """Check CUDA availability and exit if not available."""
+    import torch
+
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("Run on a machine with a CUDA-capable GPU or use HF Jobs:")
+        logger.error(
+            "  hf jobs uv run vlm-streaming-sft-unsloth.py --flavor a100-large ..."
+        )
+        sys.exit(1)
+    logger.info(f"CUDA available: {torch.cuda.get_device_name(0)}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Fine-tune VLMs with streaming datasets using Unsloth",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Quick test run
+  uv run vlm-streaming-sft-unsloth.py \\
+      --max-steps 50 \\
+      --output-repo username/vlm-test
+
+  # Full training with Trackio monitoring
+  uv run vlm-streaming-sft-unsloth.py \\
+      --max-steps 500 \\
+      --output-repo username/vlm-finetuned \\
+      --trackio-space username/trackio
+
+  # Custom dataset and model
+  uv run vlm-streaming-sft-unsloth.py \\
+      --base-model unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit \\
+      --dataset your-username/your-vlm-dataset \\
+      --max-steps 1000 \\
+      --output-repo username/custom-vlm
+        """,
+    )
+
+    # Model and data
+    parser.add_argument(
+        "--base-model",
+        default="unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit",
+        help="Base VLM model (default: unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit)",
+    )
+    parser.add_argument(
+        "--dataset",
+        default="davanstrien/iconclass-vlm-sft",
+        help="Dataset with 'images' and 'messages' columns (default: davanstrien/iconclass-vlm-sft)",
+    )
+    parser.add_argument(
+        "--output-repo",
+        required=True,
+        help="HF Hub repo to push model to (e.g., 'username/vlm-finetuned')",
+    )
+
+    # Training config
+    parser.add_argument(
+        "--num-epochs",
+        type=float,
+        default=None,
+        help="Number of epochs (default: None). Use instead of --max-steps for non-streaming mode.",
+    )
+    parser.add_argument(
+        "--max-steps",
+        type=int,
+        default=None,
+        help="Training steps (default: None). Required for streaming mode, optional otherwise.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=2,
+        help="Per-device batch size (default: 2)",
+    )
+    parser.add_argument(
+        "--gradient-accumulation",
+        type=int,
+        default=4,
+        help="Gradient accumulation steps (default: 4). Effective batch = batch-size * this",
+    )
+    parser.add_argument(
+        "--learning-rate",
+        type=float,
+        default=2e-4,
+        help="Learning rate (default: 2e-4)",
+    )
+    parser.add_argument(
+        "--max-seq-length",
+        type=int,
+        default=2048,
+        help="Maximum sequence length (default: 2048)",
+    )
+
+    # LoRA config
+    parser.add_argument(
+        "--lora-r",
+        type=int,
+        default=16,
+        help="LoRA rank (default: 16). Higher = more capacity but more VRAM",
+    )
+    parser.add_argument(
+        "--lora-alpha",
+        type=int,
+        default=16,
+        help="LoRA alpha (default: 16). Same as r per Unsloth notebook",
+    )
+
+    # Output
+    parser.add_argument(
+        "--save-local",
+        default="vlm-streaming-output",
+        help="Local directory to save model (default: vlm-streaming-output)",
+    )
+
+    # Evaluation and data control
+    parser.add_argument(
+        "--eval-split",
+        type=float,
+        default=0.0,
+        help="Fraction of data for evaluation (0.0-0.5). Default: 0.0 (no eval)",
+    )
+    parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=None,
+        help="Limit samples (default: None = use all for non-streaming, 500 for streaming)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=3407,
+        help="Random seed for reproducibility (default: 3407)",
+    )
+    parser.add_argument(
+        "--streaming",
+        action="store_true",
+        default=False,
+        help="Use streaming mode (default: False). Use for very large datasets.",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # Validate epochs/steps configuration
+    if args.streaming and args.num_epochs:
+        logger.error(
+            "Cannot use --num-epochs with --streaming. Use --max-steps instead."
+        )
+        sys.exit(1)
+    if args.streaming and not args.max_steps:
+        args.max_steps = 500  # Default for streaming
+        logger.info("Using default --max-steps=500 for streaming mode")
+    if not args.streaming and not args.num_epochs and not args.max_steps:
+        args.num_epochs = 1  # Default to 1 epoch for non-streaming
+        logger.info("Using default --num-epochs=1 for non-streaming mode")
+
+    # Determine training duration display
+    if args.num_epochs:
+        duration_str = f"{args.num_epochs} epoch(s)"
+    else:
+        duration_str = f"{args.max_steps} steps"
+
+    print("=" * 70)
+    print("VLM Fine-tuning with Unsloth")
+    print("=" * 70)
+    print("\nConfiguration:")
+    print(f"  Base model:      {args.base_model}")
+    print(f"  Dataset:         {args.dataset}")
+    print(f"  Streaming:       {args.streaming}")
+    print(
+        f"  Num samples:     {args.num_samples or ('500' if args.streaming else 'all')}"
+    )
+    print(
+        f"  Eval split:      {args.eval_split if args.eval_split > 0 else '(disabled)'}"
+    )
+    print(f"  Seed:            {args.seed}")
+    print(f"  Training:        {duration_str}")
+    print(
+        f"  Batch size:      {args.batch_size} x {args.gradient_accumulation} = {args.batch_size * args.gradient_accumulation}"
+    )
+    print(f"  Learning rate:   {args.learning_rate}")
+    print(f"  LoRA rank:       {args.lora_r}")
+    print(f"  Output repo:     {args.output_repo}")
+    print()
+
+    # Check CUDA before heavy imports
+    check_cuda()
+
+    # Enable fast transfers
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+    # Import heavy dependencies (note: import from unsloth.trainer for VLM)
+    from unsloth import FastVisionModel
+    from unsloth.trainer import UnslothVisionDataCollator
+    from datasets import load_dataset
+    from trl import SFTTrainer, SFTConfig
+    from huggingface_hub import login
+
+    # Login to Hub
+    token = os.environ.get("HF_TOKEN")
+    if token:
+        login(token=token)
+        logger.info("Logged in to Hugging Face Hub")
+    else:
+        logger.warning("HF_TOKEN not set - model upload may fail")
+
+    # 1. Load model (Qwen returns tokenizer, not processor)
+    print("\n[1/5] Loading model...")
+    start = time.time()
+
+    model, tokenizer = FastVisionModel.from_pretrained(
+        args.base_model,
+        load_in_4bit=True,
+        use_gradient_checkpointing="unsloth",
+    )
+
+    model = FastVisionModel.get_peft_model(
+        model,
+        finetune_vision_layers=True,
+        finetune_language_layers=True,
+        finetune_attention_modules=True,
+        finetune_mlp_modules=True,
+        r=args.lora_r,
+        lora_alpha=args.lora_alpha,
+        lora_dropout=0,
+        bias="none",
+        random_state=3407,
+        use_rslora=False,
+        loftq_config=None,
+    )
+    print(f"Model loaded in {time.time() - start:.1f}s")
+
+    # 2. Load dataset (streaming or non-streaming)
+    print(
+        f"\n[2/5] Loading dataset ({'streaming' if args.streaming else 'non-streaming'})..."
+    )
+    start = time.time()
+
+    if args.streaming:
+        # Streaming mode: take limited samples
+        dataset = load_dataset(args.dataset, split="train", streaming=True)
+        num_samples = args.num_samples or 500
+
+        # Peek at first sample to show info
+        sample = next(iter(dataset))
+        if "messages" in sample:
+            print(f"  Sample has {len(sample['messages'])} messages")
+        if "images" in sample:
+            img_count = (
+                len(sample["images"]) if isinstance(sample["images"], list) else 1
+            )
+            print(f"  Sample has {img_count} image(s)")
+
+        # Reload and take samples
+        dataset = load_dataset(args.dataset, split="train", streaming=True)
+        all_data = list(dataset.take(num_samples))
+        print(f"  Loaded {len(all_data)} samples in {time.time() - start:.1f}s")
+
+        if args.eval_split > 0:
+            # Manual shuffle for streaming (no built-in split)
+            import random
+
+            random.seed(args.seed)
+            random.shuffle(all_data)
+            split_idx = int(len(all_data) * (1 - args.eval_split))
+            train_data = all_data[:split_idx]
+            eval_data = all_data[split_idx:]
+            print(f"  Train: {len(train_data)} samples, Eval: {len(eval_data)} samples")
+        else:
+            train_data = all_data
+            eval_data = None
+    else:
+        # Non-streaming: use proper train_test_split
+        dataset = load_dataset(args.dataset, split="train")
+        print(f"  Dataset has {len(dataset)} total samples")
+
+        # Peek at first sample
+        sample = dataset[0]
+        if "messages" in sample:
+            print(f"  Sample has {len(sample['messages'])} messages")
+        if "images" in sample:
+            img_count = (
+                len(sample["images"]) if isinstance(sample["images"], list) else 1
+            )
+            print(f"  Sample has {img_count} image(s)")
+
+        if args.num_samples:
+            dataset = dataset.select(range(min(args.num_samples, len(dataset))))
+            print(f"  Limited to {len(dataset)} samples")
+
+        if args.eval_split > 0:
+            split = dataset.train_test_split(test_size=args.eval_split, seed=args.seed)
+            train_data = list(split["train"])
+            eval_data = list(split["test"])
+            print(f"  Train: {len(train_data)} samples, Eval: {len(eval_data)} samples")
+        else:
+            train_data = list(dataset)
+            eval_data = None
+
+        print(f"  Dataset ready in {time.time() - start:.1f}s")
+
+    # 3. Configure trainer
+    print("\n[3/5] Configuring trainer...")
+
+    # Enable training mode
+    FastVisionModel.for_training(model)
+
+    # Calculate steps per epoch for logging/eval intervals
+    effective_batch = args.batch_size * args.gradient_accumulation
+    steps_per_epoch = len(train_data) // effective_batch
+
+    # Determine run name and logging steps
+    if args.num_epochs:
+        run_name = f"vlm-sft-{args.num_epochs}ep"
+        logging_steps = max(1, steps_per_epoch // 10)  # ~10 logs per epoch
+    else:
+        run_name = f"vlm-sft-{args.max_steps}steps"
+        logging_steps = max(1, args.max_steps // 20)
+
+    training_config = SFTConfig(
+        output_dir=args.save_local,
+        per_device_train_batch_size=args.batch_size,
+        gradient_accumulation_steps=args.gradient_accumulation,
+        warmup_steps=5,  # Per notebook (not warmup_ratio)
+        num_train_epochs=args.num_epochs if args.num_epochs else 1,
+        max_steps=args.max_steps if args.max_steps else -1,  # -1 means use epochs
+        learning_rate=args.learning_rate,
+        logging_steps=logging_steps,
+        optim="adamw_8bit",  # Per notebook
+        weight_decay=0.001,
+        lr_scheduler_type="cosine" if args.num_epochs else "linear",
+        seed=args.seed,
+        # VLM-specific settings (required for Unsloth)
+        remove_unused_columns=False,
+        dataset_text_field="",
+        dataset_kwargs={"skip_prepare_dataset": True},
+        max_length=args.max_seq_length,
+        # Logging disabled for testing
+        report_to="none",
+        run_name=run_name,
+    )
+
+    # Add evaluation config if eval is enabled
+    if eval_data:
+        if args.num_epochs:
+            # For epoch-based training, eval at end of each epoch
+            training_config.eval_strategy = "epoch"
+            print("  Evaluation enabled: every epoch")
+        else:
+            training_config.eval_strategy = "steps"
+            training_config.eval_steps = max(1, args.max_steps // 5)
+            print(f"  Evaluation enabled: every {training_config.eval_steps} steps")
+
+    # Use older 'tokenizer=' parameter (not processing_class) - required for Unsloth VLM
+    trainer = SFTTrainer(
+        model=model,
+        tokenizer=tokenizer,  # Full processor, not processor.tokenizer
+        data_collator=UnslothVisionDataCollator(model, tokenizer),
+        train_dataset=train_data,
+        eval_dataset=eval_data,  # None if no eval
+        args=training_config,
+    )
+
+    # 4. Train
+    print(f"\n[4/5] Training for {duration_str}...")
+    if args.num_epochs:
+        print(
+            f"  (~{steps_per_epoch} steps/epoch, {int(steps_per_epoch * args.num_epochs)} total steps)"
+        )
+    start = time.time()
+
+    train_result = trainer.train()
+
+    train_time = time.time() - start
+    total_steps = train_result.metrics.get(
+        "train_steps", args.max_steps or steps_per_epoch * args.num_epochs
+    )
+    print(f"\nTraining completed in {train_time / 60:.1f} minutes")
+    print(f"  Speed: {total_steps / train_time:.2f} steps/s")
+
+    # Print training metrics
+    if train_result.metrics:
+        train_loss = train_result.metrics.get("train_loss")
+        if train_loss:
+            print(f"  Final train loss: {train_loss:.4f}")
+
+    # Print eval results if eval was enabled
+    if eval_data:
+        print("\nRunning final evaluation...")
+        eval_results = trainer.evaluate()
+        eval_loss = eval_results.get("eval_loss")
+        if eval_loss:
+            print(f"  Final eval loss: {eval_loss:.4f}")
+            if train_loss:
+                ratio = eval_loss / train_loss
+                if ratio > 1.5:
+                    print(
+                        f"  ⚠️  Eval loss is {ratio:.1f}x train loss - possible overfitting"
+                    )
+                else:
+                    print(
+                        f"  ✓  Eval/train ratio: {ratio:.2f} - model generalizes well"
+                    )
+
+    # 5. Save and push
+    print("\n[5/5] Saving model...")
+
+    # Save locally
+    model.save_pretrained(args.save_local)
+    tokenizer.save_pretrained(args.save_local)
+    print(f"Saved locally to {args.save_local}/")
+
+    # Push to Hub
+    print(f"\nPushing to {args.output_repo}...")
+    model.push_to_hub(args.output_repo, tokenizer=tokenizer)
+    print(f"Model available at: https://huggingface.co/{args.output_repo}")
+
+    print("\n" + "=" * 70)
+    print("Done!")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    # Show example usage if no arguments
+    if len(sys.argv) == 1:
+        print("=" * 70)
+        print("VLM Fine-tuning with Unsloth")
+        print("=" * 70)
+        print("\nFine-tune Vision-Language Models with optional train/eval split.")
+        print("\nFeatures:")
+        print("  - ~60% less VRAM with Unsloth optimizations")
+        print("  - 2x faster training vs standard methods")
+        print("  - Epoch-based or step-based training")
+        print("  - Optional evaluation to detect overfitting")
+        print("  - Trackio integration for monitoring")
+        print("\nEpoch-based training (recommended for full datasets):")
+        print("\n  uv run vlm-streaming-sft-unsloth-qwen.py \\")
+        print("      --num-epochs 1 \\")
+        print("      --eval-split 0.2 \\")
+        print("      --output-repo your-username/vlm-finetuned")
+        print("\nHF Jobs example (1 epoch with eval):")
+        print(
+            "\n  hf jobs uv run --flavor a100-large --secrets HF_TOKEN --timeout 4h -- \\"
+        )
+        print(
+            "      https://huggingface.co/datasets/uv-scripts/training/raw/main/vlm-streaming-sft-unsloth-qwen.py \\"
+        )
+        print("      --num-epochs 1 \\")
+        print("      --eval-split 0.2 \\")
+        print("      --output-repo your-username/vlm-finetuned")
+        print("\nStep-based training (for streaming or quick tests):")
+        print("\n  uv run vlm-streaming-sft-unsloth-qwen.py \\")
+        print("      --streaming \\")
+        print("      --max-steps 500 \\")
+        print("      --output-repo your-username/vlm-finetuned")
+        print("\nFor full help: uv run vlm-streaming-sft-unsloth-qwen.py --help")
+        print("=" * 70)
+        sys.exit(0)
+
+    main()