Upload train_sft_qwen25.py with huggingface_hub

train_sft_qwen25.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "datasets>=2.18.0", "trackio"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+model_id = "Qwen/Qwen2.5-0.5B"
+
+# Small subset for a quick test run
+dataset = load_dataset("trl-lib/Capybara", split="train")
+dataset = dataset.shuffle(seed=42).select(range(500))
+
+# Train/eval split for monitoring
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+trainer = SFTTrainer(
+    model=model_id,
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=peft_config,
+    args=SFTConfig(
+        output_dir="qwen2.5-0.5b-sft-demo",
+        push_to_hub=True,
+        hub_model_id="davidsmts/qwen2.5-0.5b-sft-demo",
+        hub_strategy="every_save",
+        num_train_epochs=1,
+        per_device_train_batch_size=1,
+        gradient_accumulation_steps=8,
+        max_length=512,
+        eval_strategy="steps",
+        eval_steps=50,
+        save_strategy="steps",
+        save_steps=100,
+        logging_steps=10,
+        report_to="trackio",
+        project="qwen2.5-sft-demo",
+        run_name="qwen2.5-0.5b-capybara",
+    ),
+)
+
+trainer.train()
+trainer.push_to_hub()