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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "datasets", "transformers", "accelerate", "bitsandbytes"]
# ///
from datasets import load_dataset, concatenate_datasets
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
print("Loading datasets...")
# Dataset 1: Codeforces competitive programming (messages format)
ds1 = load_dataset(
"open-r1/codeforces-cots",
"solutions_w_editorials_py_decontaminated",
split="train"
)
ds1 = ds1.select_columns(["messages"])
print(f"Codeforces: {len(ds1)} examples")
# Dataset 2: Golang coder (messages format)
ds2 = load_dataset("smcleod/golang-coder", split="train")
ds2 = ds2.select_columns(["messages"])
print(f"Golang: {len(ds2)} examples")
# Dataset 3: Vue/Nuxt/Tailwind (text format - needs conversion)
ds3_raw = load_dataset("kevind13/vuejs-nuxt-tailwind-codellama", split="train")
# Convert text format to messages format for consistency
def text_to_messages(example):
return {"messages": [{"role": "user", "content": "Continue the code."}, {"role": "assistant", "content": example["text"]}]}
ds3 = ds3_raw.map(text_to_messages, remove_columns=ds3_raw.column_names)
print(f"Vue/Nuxt/Tailwind: {len(ds3)} examples")
# Dataset 4: React code instructions (messages format)
ds4 = load_dataset("cfahlgren1/react-code-instructions", split="train")
ds4 = ds4.select_columns(["messages"])
print(f"React: {len(ds4)} examples")
# Concatenate all datasets
combined = concatenate_datasets([ds1, ds2, ds3, ds4])
combined = combined.shuffle(seed=42)
print(f"Combined dataset: {len(combined)} examples")
# Create train/eval split
dataset_split = combined.train_test_split(test_size=0.05, seed=42)
print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
print("Starting training...")
trainer = SFTTrainer(
model="Qwen/Qwen3-0.6B",
train_dataset=dataset_split["train"],
eval_dataset=dataset_split["test"],
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",
),
args=SFTConfig(
output_dir="qwen3-0.6b-multicode",
push_to_hub=True,
hub_model_id="chaddy81/qwen3-0.6b-multicode-sft",
hub_private_repo=False,
num_train_epochs=1,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
gradient_accumulation_steps=8,
gradient_checkpointing=True,
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.05,
logging_steps=10,
save_strategy="steps",
save_steps=500,
eval_strategy="steps",
eval_steps=500,
hub_strategy="every_save",
bf16=True,
report_to="trackio",
project="qwen3-multicode",
run_name="qwen3-0.6b-sft-multicode",
)
)
trainer.train()
trainer.push_to_hub()
print("Training complete! Model pushed to Hub.")
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