File size: 3,117 Bytes
4e393ba c19b216 4e393ba c19b216 4e393ba c19b216 1e0027e 4e393ba 1e0027e 4e393ba |
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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
# /// script
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.45.0",
# "accelerate>=0.24.0",
# "trackio",
# "datasets",
# ]
# ///
"""
Fine-tune Qwen3-0.6B on open-r1/codeforces-cots for competitive programming.
"""
import trackio
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoTokenizer
# Load dataset - using the solutions config with messages column
print("Loading dataset...")
dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train")
print(f"Dataset loaded: {len(dataset)} examples")
# The dataset has a 'messages' column in chat format
# We need to keep only the 'messages' column for SFT training
print("Preparing dataset - keeping only messages column...")
dataset = dataset.select_columns(["messages"])
# Create train/eval split
print("Creating train/eval split...")
dataset_split = dataset.train_test_split(test_size=0.05, seed=42)
train_dataset = dataset_split["train"]
eval_dataset = dataset_split["test"]
print(f"Train: {len(train_dataset)} examples")
print(f"Eval: {len(eval_dataset)} examples")
# Load tokenizer for chat template
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Processing function to convert messages to text using chat template
def formatting_func(example):
return tokenizer.apply_chat_template(example["messages"], tokenize=False)
# Training configuration
config = SFTConfig(
# Hub settings - CRITICAL for saving results
output_dir="qwen3-codeforces-sft",
push_to_hub=True,
hub_model_id="kintopp/qwen3-0.6b-codeforces-cots",
hub_strategy="every_save",
# Training parameters
num_train_epochs=1,
per_device_train_batch_size=2,
gradient_accumulation_steps=8,
learning_rate=2e-5,
max_length=2048,
# Logging & checkpointing
logging_steps=25,
save_strategy="steps",
save_steps=500,
save_total_limit=2,
# Evaluation
eval_strategy="steps",
eval_steps=500,
# Optimization
warmup_ratio=0.1,
lr_scheduler_type="cosine",
gradient_checkpointing=True,
bf16=True,
# Monitoring with Trackio
report_to="trackio",
project="qwen3-codeforces",
run_name="qwen3-0.6b-codeforces-sft",
)
# LoRA configuration for efficient training
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)
# Initialize trainer
print("Initializing trainer...")
trainer = SFTTrainer(
model="Qwen/Qwen3-0.6B",
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=config,
peft_config=peft_config,
formatting_func=formatting_func,
)
print("Starting training...")
trainer.train()
print("Pushing final model to Hub...")
trainer.push_to_hub()
print("Complete! Model at: https://huggingface.co/kintopp/qwen3-0.6b-codeforces-cots")
|