Upload vlm-streaming-sft-unsloth.py with huggingface_hub

vlm-streaming-sft-unsloth.py

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+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "unsloth",
+#     "datasets",
+#     "trl",
+#     "huggingface_hub[hf_transfer]",
+#     "trackio",
+# ]
+# ///
+"""
+Fine-tune Vision Language Models using streaming datasets and Unsloth optimizations.
+
+Streams data directly from the Hub - no disk space needed for massive VLM datasets.
+Uses Unsloth for ~60% less VRAM and 2x faster training.
+
+Run locally (if you have a GPU):
+    uv run vlm-streaming-sft-unsloth.py \
+        --max-steps 100 \
+        --output-repo your-username/vlm-test
+
+Run on HF Jobs:
+    hf jobs uv run vlm-streaming-sft-unsloth.py \
+        --flavor a100-large \
+        --secrets HF_TOKEN \
+        -- \
+        --max-steps 500 \
+        --output-repo your-username/vlm-finetuned
+
+With Trackio dashboard:
+    uv run vlm-streaming-sft-unsloth.py \
+        --max-steps 500 \
+        --output-repo your-username/vlm-finetuned \
+        --trackio-space your-username/trackio
+"""
+
+import argparse
+import logging
+import os
+import sys
+import time
+
+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(
+        "--max-steps",
+        type=int,
+        default=500,
+        help="Training steps (default: 500). Required for streaming datasets.",
+    )
+    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=32,
+        help="LoRA alpha (default: 32). Usually 2*r",
+    )
+
+    # Logging
+    parser.add_argument(
+        "--trackio-space",
+        default=None,
+        help="HF Space for Trackio dashboard (e.g., 'username/trackio')",
+    )
+    parser.add_argument(
+        "--save-local",
+        default="vlm-streaming-output",
+        help="Local directory to save model (default: vlm-streaming-output)",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    print("=" * 70)
+    print("VLM Streaming Fine-tuning with Unsloth")
+    print("=" * 70)
+    print("\nConfiguration:")
+    print(f"  Base model:      {args.base_model}")
+    print(f"  Dataset:         {args.dataset}")
+    print(f"  Max steps:       {args.max_steps}")
+    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(f"  Trackio space:   {args.trackio_space or '(not configured)'}")
+    print()
+
+    # Check CUDA before heavy imports
+    check_cuda()
+
+    # Enable fast transfers
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+    # Set Trackio space if provided
+    if args.trackio_space:
+        os.environ["TRACKIO_SPACE_ID"] = args.trackio_space
+        logger.info(f"Trackio dashboard: https://huggingface.co/spaces/{args.trackio_space}")
+
+    # Import heavy dependencies
+    from unsloth import FastVisionModel, 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
+    print("\n[1/5] Loading model...")
+    start = time.time()
+
+    model, tokenizer = FastVisionModel.from_pretrained(
+        args.base_model,
+        max_seq_length=args.max_seq_length,
+        load_in_4bit=True,
+    )
+
+    model = FastVisionModel.get_peft_model(
+        model,
+        finetune_vision_layers=False,
+        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,
+        use_gradient_checkpointing="unsloth",
+    )
+
+    model = FastVisionModel.for_training(model)
+    print(f"Model loaded in {time.time() - start:.1f}s")
+
+    # 2. Load streaming dataset
+    print("\n[2/5] Loading streaming dataset...")
+    start = time.time()
+
+    dataset = load_dataset(
+        args.dataset,
+        split="train",
+        streaming=True,
+    )
+
+    # Peek at first sample
+    sample = next(iter(dataset))
+    print(f"Dataset ready in {time.time() - start:.1f}s")
+    if "messages" in sample:
+        print(f"  Sample has {len(sample['messages'])} messages")
+    if "images" in sample:
+        print(f"  Sample has {len(sample['images']) if isinstance(sample['images'], list) else 1} image(s)")
+
+    # 3. Configure trainer
+    print("\n[3/5] Configuring trainer...")
+
+    training_config = SFTConfig(
+        output_dir=args.save_local,
+        per_device_train_batch_size=args.batch_size,
+        gradient_accumulation_steps=args.gradient_accumulation,
+        max_steps=args.max_steps,
+        learning_rate=args.learning_rate,
+        warmup_steps=min(10, args.max_steps // 10),
+        logging_steps=max(1, args.max_steps // 20),
+        optim="adamw_8bit",
+        weight_decay=0.01,
+        lr_scheduler_type="cosine",
+        seed=3407,
+        bf16=True,
+        # VLM-specific settings (required for Unsloth)
+        remove_unused_columns=False,
+        dataset_text_field="",
+        dataset_kwargs={"skip_prepare_dataset": True},
+        max_seq_length=args.max_seq_length,
+        # Logging
+        report_to="trackio",
+        run_name=f"vlm-streaming-{args.max_steps}steps",
+    )
+
+    trainer = SFTTrainer(
+        model=model,
+        tokenizer=tokenizer,
+        data_collator=UnslothVisionDataCollator(model, tokenizer),
+        train_dataset=dataset,
+        args=training_config,
+    )
+
+    # 4. Train
+    print(f"\n[4/5] Training for {args.max_steps} steps...")
+    start = time.time()
+
+    trainer.train()
+
+    train_time = time.time() - start
+    print(f"\nTraining completed in {train_time / 60:.1f} minutes")
+    print(f"  Speed: {args.max_steps / train_time:.2f} steps/s")
+
+    # 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}")
+
+    # Quick inference test
+    print("\n" + "=" * 70)
+    print("Quick inference test:")
+    print("=" * 70)
+
+    FastVisionModel.for_inference(model)
+
+    # Create a simple test prompt
+    test_messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Describe what you see in this image."},
+            ],
+        }
+    ]
+
+    inputs = tokenizer.apply_chat_template(
+        test_messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to("cuda")
+
+    print("\nTest prompt: 'Describe what you see in this image.'")
+    print("(Note: No image provided - this just tests the model loads correctly)")
+
+    outputs = model.generate(
+        input_ids=inputs,
+        max_new_tokens=32,
+        temperature=0.7,
+        top_p=0.8,
+        do_sample=True,
+    )
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print(f"Response preview: {response[:200]}...")
+
+    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 Streaming Fine-tuning with Unsloth")
+        print("=" * 70)
+        print("\nFine-tune Vision-Language Models using streaming datasets.")
+        print("Data streams directly from the Hub - no disk space needed.")
+        print("\nFeatures:")
+        print("  - ~60% less VRAM with Unsloth optimizations")
+        print("  - 2x faster training vs standard methods")
+        print("  - Trackio integration for monitoring")
+        print("  - Works with any VLM dataset in conversation format")
+        print("\nExample usage:")
+        print("\n  uv run vlm-streaming-sft-unsloth.py \\")
+        print("      --max-steps 500 \\")
+        print("      --output-repo your-username/vlm-finetuned")
+        print("\nHF Jobs example:")
+        print("\n  hf jobs uv run vlm-streaming-sft-unsloth.py \\")
+        print("      --flavor a100-large \\")
+        print("      --secrets HF_TOKEN \\")
+        print("      -- \\")
+        print("      --max-steps 500 \\")
+        print("      --output-repo your-username/vlm-finetuned")
+        print("\nFor full help: uv run vlm-streaming-sft-unsloth.py --help")
+        print("=" * 70)
+        sys.exit(0)
+
+    main()