add qwen sft uv script

scripts/sft_qwen_uv.py

@@ -0,0 +1,92 @@
+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "datasets>=2.16.0",
+#     "trackio",
+# ]
+# ///
+
+import os
+import trackio
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+
+def main() -> None:
+    base_model = "Qwen/Qwen2.5-0.5B"
+    hub_model_id = os.environ.get("HUB_MODEL_ID", "davidsmts/qwen25-0_5b-sft-demo")
+    project = os.environ.get("TRACKIO_PROJECT", "qwen25_sft_demo")
+    run_name = os.environ.get("TRACKIO_RUN", "qwen25-0_5b-sft-lora")
+
+    print("Loading dataset...")
+    dataset = load_dataset("trl-lib/Capybara", split="train")
+    print(f"Loaded {len(dataset)} examples")
+
+    print("Creating train/eval split...")
+    dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+    train_ds = dataset_split["train"]
+    eval_ds = dataset_split["test"]
+    print(f"Train {len(train_ds)}, Eval {len(eval_ds)}")
+
+    trackio.init(
+        project=project,
+        run_name=run_name,
+        config={"model": base_model, "dataset": "trl-lib/Capybara"},
+    )
+
+    peft_config = LoraConfig(
+        r=16,
+        lora_alpha=32,
+        lora_dropout=0.05,
+        bias="none",
+        task_type="CAUSAL_LM",
+        target_modules=["q_proj", "v_proj"],
+    )
+
+    training_args = SFTConfig(
+        output_dir="qwen25-0_5b-sft-demo",
+        push_to_hub=True,
+        hub_model_id=hub_model_id,
+        hub_strategy="every_save",
+        num_train_epochs=1,
+        per_device_train_batch_size=4,
+        gradient_accumulation_steps=4,
+        learning_rate=2e-5,
+        logging_steps=10,
+        save_strategy="steps",
+        save_steps=50,
+        save_total_limit=2,
+        eval_strategy="steps",
+        eval_steps=50,
+        warmup_ratio=0.1,
+        lr_scheduler_type="cosine",
+        gradient_checkpointing=True,
+        fp16=True,
+        report_to="trackio",
+        project=project,
+        run_name=run_name,
+    )
+
+    print("Initializing trainer...")
+    trainer = SFTTrainer(
+        model=base_model,
+        args=training_args,
+        train_dataset=train_ds,
+        eval_dataset=eval_ds,
+        peft_config=peft_config,
+    )
+
+    print("Starting training...")
+    trainer.train()
+
+    print("Pushing to Hub...")
+    trainer.push_to_hub()
+    print(f"Complete! Model available at https://huggingface.co/{hub_model_id}")
+
+
+if __name__ == "__main__":
+    main()