Upload train_qwen3_hf_v3.py with huggingface_hub

train_qwen3_hf_v3.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "transformers>=4.45.0", "datasets", "accelerate", "torch"]
+# ///
+"""Fine-tune Qwen3-0.6B on CodeForces-CoTS (100 examples)"""
+
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import torch
+
+print(f"CUDA available: {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    print(f"GPU: {torch.cuda.get_device_name(0)}")
+    print(f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
+
+# Load 100 examples
+print("\nLoading dataset...")
+dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train").select(range(100))
+print(f"Dataset: {len(dataset)} examples")
+
+# Format for SFT: use the 'messages' column directly (already in chat format)
+# The dataset has 'messages' column with list of {'role': ..., 'content': ...}
+print(f"Sample messages: {dataset[0]['messages'][:1]}")
+
+# Split: 90 train, 10 val
+splits = dataset.train_test_split(test_size=0.1, seed=42)
+train_ds, val_ds = splits["train"], splits["test"]
+print(f"Train: {len(train_ds)}, Val: {len(val_ds)}")
+
+peft_config = LoraConfig(
+    r=8,
+    lora_alpha=16,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# 90 examples, batch=1, accum=4 -> ~22 steps/epoch
+training_args = SFTConfig(
+    output_dir="./qwen3-0.6b-codeforces-cots",
+    num_train_epochs=1,
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    warmup_ratio=0.1,
+    logging_steps=2,
+    logging_first_step=True,
+    save_strategy="no",
+    eval_strategy="steps",
+    eval_steps=5,
+    max_length=1024,
+    push_to_hub=True,
+    hub_model_id="gilbaes/qwen3-0.6b-codeforces-cots",
+    report_to="none",
+    bf16=True,
+    gradient_checkpointing=True,
+    optim="adamw_torch_fused",
+    dataset_text_field=None,  # Use messages format
+)
+
+print("\nInitializing trainer...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=train_ds,
+    eval_dataset=val_ds,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+print(f"Trainable params: {trainer.model.num_parameters(only_trainable=True):,}")
+print(f"Total params: {trainer.model.num_parameters():,}")
+
+print("\n" + "="*50)
+print("TRAINING START")
+print("="*50 + "\n")
+
+trainer.train()
+
+print("\n" + "="*50)
+print("PUSHING TO HUB")
+print("="*50)
+trainer.push_to_hub()
+print("\nDone! Model: https://huggingface.co/gilbaes/qwen3-0.6b-codeforces-cots")