Upload train_qwen3_codeforces_v2.py with huggingface_hub

train_qwen3_codeforces_v2.py

@@ -0,0 +1,96 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.45.0",
+#     "datasets>=2.18.0",
+#     "accelerate>=0.30.0",
+#     "torch>=2.0.0",
+# ]
+# ///
+
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B", trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B",
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+
+print("Loading dataset...")
+dataset = load_dataset("open-r1/codeforces-cots", "solutions_py_decontaminated", split="train")
+print(f"Dataset size: {len(dataset)}")
+
+# Take a subset for faster training (full dataset is large)
+dataset = dataset.shuffle(seed=42).select(range(min(10000, len(dataset))))
+print(f"Using {len(dataset)} examples")
+
+# Split
+split = dataset.train_test_split(test_size=0.05, seed=42)
+train_dataset = split["train"]
+eval_dataset = split["test"]
+
+print("Setting up LoRA...")
+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",
+)
+
+print("Setting up training...")
+training_args = SFTConfig(
+    output_dir="qwen3-0.6b-codeforces-sft",
+    push_to_hub=True,
+    hub_model_id="luiscosio/qwen3-0.6b-codeforces-sft",
+    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=100,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=3,
+    logging_steps=10,
+    bf16=True,
+    max_length=2048,
+    report_to="none",
+)
+
+print("Creating trainer...")
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Saving and pushing to Hub...")
+trainer.save_model()
+trainer.push_to_hub()
+
+print("Done!")