Upload train_qwen3_codeforces.py with huggingface_hub

train_qwen3_codeforces.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers", "datasets", "accelerate", "torch"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+# Load the Codeforces CoTs dataset (decontaminated version)
+print("Loading dataset...")
+dataset = load_dataset("open-r1/codeforces-cots", "solutions_py_decontaminated", split="train")
+print(f"Dataset size: {len(dataset)} examples")
+
+# Create train/eval split
+dataset_split = dataset.train_test_split(test_size=0.05, seed=42)
+print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
+
+# LoRA config for efficient fine-tuning
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+# Training config
+training_args = SFTConfig(
+    output_dir="qwen3-0.6b-codeforces-sft",
+    push_to_hub=True,
+    hub_model_id="luiscosio/qwen3-0.6b-codeforces-sft",
+    hub_strategy="every_save",
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=8,
+    gradient_checkpointing=True,
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.1,
+    eval_strategy="steps",
+    eval_steps=200,
+    save_strategy="steps",
+    save_steps=200,
+    save_total_limit=3,
+    logging_steps=10,
+    report_to="trackio",
+    run_name="qwen3-0.6b-codeforces-sft",
+    bf16=True,
+    optim="adamw_torch_fused",
+    max_grad_norm=1.0,
+    max_length=2048,
+)
+
+# Initialize trainer
+print("Initializing trainer...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# Train
+print("Starting training...")
+trainer.train()
+
+# Push final model to Hub
+print("Pushing to Hub...")
+trainer.push_to_hub()
+print("Training complete!")