Upload test_training_llama_small_batch.py with huggingface_hub

test_training_llama_small_batch.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.40.0", "datasets>=2.18.0", "accelerate>=0.28.0"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+print("=" * 80)
+print("TEST RUN: Biomedical Llama Fine-Tuning (100 examples)")
+print("=" * 80)
+
+print("\n[1/4] Loading dataset...")
+dataset = load_dataset("panikos/biomedical-llama-training")
+
+# Use first 100 examples for test
+train_dataset = dataset["train"].select(range(100))
+eval_dataset = dataset["validation"].select(range(20))
+
+print(f"  Train: {len(train_dataset)} examples")
+print(f"  Eval: {len(eval_dataset)} examples")
+
+print("\n[2/4] Configuring LoRA...")
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+print("  LoRA rank: 16, alpha: 32")
+
+print("\n[3/4] Initializing trainer...")
+trainer = SFTTrainer(
+    model="meta-llama/Llama-3.1-8B-Instruct",
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=lora_config,
+    args=SFTConfig(
+        output_dir="llama-biomedical-test",
+        num_train_epochs=1,
+        per_device_train_batch_size=1,  # REDUCED from 2 to 1
+        gradient_accumulation_steps=8,  # INCREASED from 4 to 8
+        learning_rate=2e-4,
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.1,
+        logging_steps=5,
+        eval_strategy="steps",
+        eval_steps=20,
+        save_strategy="epoch",
+        push_to_hub=True,
+        hub_model_id="panikos/llama-biomedical-test",
+        hub_private_repo=True,
+        bf16=True,
+        gradient_checkpointing=False,  # DISABLED for stability
+        report_to="trackio",
+        project="biomedical-llama-training",
+        run_name="test-run-100-examples-v3"
+    )
+)
+
+print("\n[4/4] Starting training...")
+print("  Model: meta-llama/Llama-3.1-8B-Instruct")
+print("  Method: SFT with LoRA")
+print("  Epochs: 1")
+print("  Batch size: 1 x 8 = 8 (effective) - optimized for memory")
+print("  Gradient checkpointing: DISABLED")
+print()
+
+trainer.train()
+
+print("\n" + "=" * 80)
+print("Pushing model to Hub...")
+print("=" * 80)
+trainer.push_to_hub()
+
+print("\n" + "=" * 80)
+print("TEST COMPLETE!")
+print("=" * 80)
+print("\nModel: https://huggingface.co/panikos/llama-biomedical-test")
+print("Dashboard: https://panikos-trackio.hf.space/")
+print("Ready for full production training!")