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

vlm-streaming-sft-unsloth.py

@@ -6,6 +6,8 @@
 #     "trl",
 #     "huggingface_hub[hf_transfer]",
 #     "trackio",
+#     "transformers==4.56.2",
+#     "trl==0.22.2",
 # ]
 # ///
 """
@@ -90,8 +92,8 @@ Examples:
     # Model and data
     parser.add_argument(
         "--base-model",
-        default="unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
-        help="Base VLM model (default: unsloth/gemma-3-4b-it-unsloth-bnb-4bit)",
+        default="unsloth/gemma-3-4b-pt",
+        help="Base VLM model (default: unsloth/gemma-3-4b-pt)",
     )
     parser.add_argument(
         "--dataset",
@@ -195,8 +197,9 @@ def main():
         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
+    # 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
@@ -213,17 +216,15 @@ def main():
     print("\n[1/5] Loading model...")
     start = time.time()
 
-    model, tokenizer = FastVisionModel.from_pretrained(
+    model, processor = FastVisionModel.from_pretrained(
         args.base_model,
-        max_seq_length=args.max_seq_length,
         load_in_4bit=True,
-        dtype=None,  # Auto-detect
-        fast_inference=False,  # For training
+        use_gradient_checkpointing="unsloth",
     )
 
     model = FastVisionModel.get_peft_model(
         model,
-        finetune_vision_layers=False,
+        finetune_vision_layers=True,
         finetune_language_layers=True,
         finetune_attention_modules=True,
         finetune_mlp_modules=True,
@@ -233,10 +234,9 @@ def main():
         bias="none",
         random_state=3407,
         use_rslora=False,
-        use_gradient_checkpointing="unsloth",
+        loftq_config=None,
+        target_modules="all-linear",
     )
-
-    model = FastVisionModel.for_training(model)
     print(f"Model loaded in {time.time() - start:.1f}s")
 
     # 2. Load streaming dataset
@@ -249,35 +249,49 @@ def main():
         streaming=True,
     )
 
-    # Peek at first sample
+    # Peek at first sample to show info
     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)")
+        img_count = len(sample['images']) if isinstance(sample['images'], list) else 1
+        print(f"  Sample has {img_count} image(s)")
+
+    # Reload dataset (consumed one sample above)
+    dataset = load_dataset(
+        args.dataset,
+        split="train",
+        streaming=True,
+    )
 
     # 3. Configure trainer
     print("\n[3/5] Configuring trainer...")
 
+    # Enable training mode
+    FastVisionModel.for_training(model)
+
     training_config = SFTConfig(
         output_dir=args.save_local,
         per_device_train_batch_size=args.batch_size,
         gradient_accumulation_steps=args.gradient_accumulation,
+        gradient_checkpointing=True,
+        gradient_checkpointing_kwargs={"use_reentrant": False},
+        max_grad_norm=0.3,
+        warmup_ratio=0.03,
         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,
+        save_strategy="steps",
+        optim="adamw_torch_fused",
+        weight_decay=0.001,
         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,
+        max_length=args.max_seq_length,
         # Logging
         report_to="trackio",
         run_name=f"vlm-streaming-{args.max_steps}steps",
@@ -285,11 +299,10 @@ def main():
 
     trainer = SFTTrainer(
         model=model,
-        tokenizer=tokenizer,
-        data_collator=UnslothVisionDataCollator(model, tokenizer),
         train_dataset=dataset,
+        processing_class=processor.tokenizer,
+        data_collator=UnslothVisionDataCollator(model, processor),
         args=training_config,
-        processing_class=tokenizer,  # Required for Unsloth to detect VLM
     )
 
     # 4. Train
@@ -307,51 +320,15 @@ def main():
 
     # Save locally
     model.save_pretrained(args.save_local)
-    tokenizer.save_pretrained(args.save_local)
+    processor.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)
+    model.push_to_hub(args.output_repo)
+    processor.push_to_hub(args.output_repo)
     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)