Upload train_qwen3_hf_v2.py with huggingface_hub
Browse files- train_qwen3_hf_v2.py +83 -0
train_qwen3_hf_v2.py
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# /// script
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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "transformers>=4.45.0", "datasets", "accelerate", "torch"]
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# ///
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"""Fine-tune Qwen3-0.6B on CodeForces-CoTS (100 examples)"""
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import os
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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from datasets import load_dataset
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from peft import LoraConfig
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from trl import SFTTrainer, SFTConfig
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import torch
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print(f"CUDA available: {torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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print(f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
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# Load 100 examples
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print("\nLoading dataset...")
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dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train").select(range(100))
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print(f"Dataset: {len(dataset)} examples")
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# Split: 90 train, 10 val
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splits = dataset.train_test_split(test_size=0.1, seed=42)
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train_ds, val_ds = splits["train"], splits["test"]
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print(f"Train: {len(train_ds)}, Val: {len(val_ds)}")
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peft_config = LoraConfig(
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r=8,
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lora_alpha=16,
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lora_dropout=0.05,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
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bias="none",
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task_type="CAUSAL_LM"
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)
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# 90 examples, batch=1, accum=4 -> ~22 steps/epoch
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# logging every 2 steps = every ~8 examples
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training_args = SFTConfig(
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output_dir="./qwen3-0.6b-codeforces-cots",
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num_train_epochs=1,
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per_device_train_batch_size=1,
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gradient_accumulation_steps=4,
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learning_rate=2e-4,
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warmup_ratio=0.1,
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logging_steps=2, # Log every ~8 examples
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logging_first_step=True,
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save_strategy="no",
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eval_strategy="steps",
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eval_steps=5, # Eval every ~20 examples
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max_length=1024,
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push_to_hub=True,
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hub_model_id="gilbaes/qwen3-0.6b-codeforces-cots",
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report_to="none",
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bf16=True,
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gradient_checkpointing=True,
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optim="adamw_torch_fused",
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)
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print("\nInitializing trainer...")
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trainer = SFTTrainer(
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model="Qwen/Qwen3-0.6B",
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train_dataset=train_ds,
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eval_dataset=val_ds,
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peft_config=peft_config,
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args=training_args,
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)
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print(f"Trainable params: {trainer.model.num_parameters(only_trainable=True):,}")
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print(f"Total params: {trainer.model.num_parameters():,}")
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print("\n" + "="*50)
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print("TRAINING START")
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print("="*50 + "\n")
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trainer.train()
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print("\n" + "="*50)
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print("PUSHING TO HUB")
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print("="*50)
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trainer.push_to_hub()
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print("\nDone! Model: https://huggingface.co/gilbaes/qwen3-0.6b-codeforces-cots")
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