Upload test_qwen25.py with huggingface_hub

test_qwen25.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "datasets",
+#     "torch",
+# ]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+print("Loading dataset...")
+dataset = load_dataset("trl-lib/Capybara", split="train")
+dataset = dataset.shuffle(seed=42).select(range(500))
+print(f"Using {len(dataset)} examples")
+
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+
+config = SFTConfig(
+    output_dir="qwen25-test",
+    push_to_hub=True,
+    hub_model_id="luiscosio/qwen25-test",
+    num_train_epochs=1,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    logging_steps=10,
+    save_strategy="epoch",
+    bf16=True,
+    report_to="none",
+)
+
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "v_proj"],
+)
+
+print("Initializing trainer with Qwen2.5-0.5B...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=dataset_split["train"],
+    args=config,
+    peft_config=peft_config,
+)
+
+print("Starting training...")
+trainer.train()
+trainer.push_to_hub()
+print("Done!")