File size: 2,481 Bytes
6b779ab 3e3fc0e 6b779ab 3e3fc0e 6b779ab 3e3fc0e 6b779ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
# /// 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")
print(f"Columns: {dataset.column_names}")
# Check first example to understand structure
print(f"First example keys: {dataset[0].keys()}")
if "messages" in dataset.column_names:
print(f"Messages sample: {dataset[0]['messages'][:2] if len(dataset[0]['messages']) > 1 else dataset[0]['messages']}")
# 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 - using "messages" column for chat format
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,
dataset_text_field=None, # Use messages format
)
# 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!")
|