Upload train_qwen3_hf.py with huggingface_hub

train_qwen3_hf.py

@@ -0,0 +1,56 @@
+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "transformers>=4.45.0", "datasets", "accelerate", "torch"]
+# ///
+"""Fine-tune Qwen3-0.6B on CodeForces-CoTS (100 examples)"""
+
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import torch
+
+print(f"CUDA available: {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    print(f"GPU: {torch.cuda.get_device_name(0)}")
+
+dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train").select(range(100))
+print(f"Dataset: {len(dataset)} examples")
+
+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_args = SFTConfig(
+    output_dir="./qwen3-0.6b-codeforces-cots",
+    num_train_epochs=1,
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=8,
+    learning_rate=2e-4,
+    warmup_ratio=0.1,
+    logging_steps=5,
+    save_strategy="no",
+    eval_strategy="no",
+    max_length=2048,
+    push_to_hub=True,
+    hub_model_id="gilbaes/qwen3-0.6b-codeforces-cots",
+    report_to="none",
+    bf16=True,
+    gradient_checkpointing=True,
+    optim="adamw_torch_fused",
+)
+
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=dataset,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+print(f"Trainable params: {trainer.model.num_parameters(only_trainable=True):,}")
+trainer.train()
+trainer.push_to_hub()
+print("Done! Model at gilbaes/qwen3-0.6b-codeforces-cots")