Upload main.py with huggingface_hub

main.py

@@ -0,0 +1,173 @@
+# /// script
+# dependencies = [
+#     "trl",
+#     "peft",
+#     "datasets",
+#     "transformers",
+#     "accelerate",
+#     "torch",
+#     "deepspeed",
+# ]
+# ///
+
+import inspect
+
+import datasets
+import trl.experimental.gold as gold
+from transformers import AutoTokenizer
+
+
+# -----------------------------
+# Models
+# -----------------------------
+
+STUDENT_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
+TEACHER_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"
+
+OUTPUT_DIR = "gold-code-deepspeed-test"
+
+
+# -----------------------------
+#
+# If ZeRO-3 is painfully slow, try this instead:
+
+DS_CONFIG = {
+     "zero_optimization": {
+         "stage": 2,
+         "offload_optimizer": {
+             "device": "cpu",
+             "pin_memory": True,
+         },
+         "overlap_comm": True,
+         "contiguous_gradients": True,
+     },
+     "bf16": {
+         "enabled": True,
+     },
+     "train_micro_batch_size_per_gpu": "auto",
+     "gradient_accumulation_steps": "auto",
+     "gradient_clipping": "auto",
+}
+
+
+# -----------------------------
+# Dataset
+# -----------------------------
+
+def to_messages(example):
+    description = str(example.get("description", "")).strip()
+
+    if not description:
+        description = str(example)
+
+    # Keep prompts short at first. code_contests descriptions can be long.
+    description = description[:6000]
+
+    return {
+        "messages": [
+            {
+                "role": "system",
+                "content": (
+                    "You are a careful competitive programming assistant. "
+                    "Return only the final correct solution code. "
+                    "Do not include markdown or explanations."
+                ),
+            },
+            {
+                "role": "user",
+                "content": (
+                    "Solve this programming problem:\n\n"
+                    f"{description}"
+                ),
+            },
+        ]
+    }
+
+
+def main():
+    print("Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(
+        STUDENT_MODEL,
+        trust_remote_code=True,
+    )
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    print("Loading dataset...")
+    raw = datasets.load_dataset(
+        "deepmind/code_contests",
+        split="train[:100]",
+    )
+
+    print("Raw columns:", raw.column_names)
+
+    train_dataset = raw.map(
+        to_messages,
+        remove_columns=raw.column_names,
+    )
+
+    print("Processed example:")
+    print(train_dataset[0])
+
+    config = gold.GOLDConfig(
+        output_dir=OUTPUT_DIR,
+
+        # GOLD generation settings
+        temperature=0.8,
+        top_p=0.95,
+        max_length=1024,
+
+        # Training settings
+        max_steps=24,
+        per_device_train_batch_size=1,
+        gradient_accumulation_steps=4,
+        learning_rate=5e-6,
+
+        # Logging/saving
+        logging_steps=1,
+        save_steps=12,
+        report_to="none",
+
+        # Precision
+        bf16=True,
+
+        # DeepSpeed
+        deepspeed=DS_CONFIG,
+    )
+
+    # TRL versions differ: some use processing_class, some older ones use tokenizer.
+    trainer_kwargs = {
+        "model": STUDENT_MODEL,
+        "teacher_model": TEACHER_MODEL,
+        "args": config,
+        "train_dataset": train_dataset,
+    }
+
+    signature = inspect.signature(gold.GOLDTrainer)
+
+    if "processing_class" in signature.parameters:
+        trainer_kwargs["processing_class"] = tokenizer
+    elif "tokenizer" in signature.parameters:
+        trainer_kwargs["tokenizer"] = tokenizer
+    else:
+        print("Warning: GOLDTrainer signature has no processing_class/tokenizer parameter.")
+
+    print("Building GOLDTrainer...")
+    trainer = gold.GOLDTrainer(**trainer_kwargs)
+
+    print("Training...")
+    trainer.train()
+
+    print("Saving...")
+    trainer.save_model(OUTPUT_DIR)
+
+    # Optional push
+    trainer.push_to_hub("moos124/gold-code-deepspeed-test")
+
+
+if __name__ == "__main__":
+    main()