Upload train_google_api.py

train_google_api.py

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+"""
+Fine-tune Qwen2.5-Coder-3B-Instruct on Google Classroom & Drive API code.
+Uses LoRA via PEFT for memory-efficient training.
+"""
+import os
+from datasets import load_dataset
+from trl import SFTTrainer, SFTConfig
+from peft import LoraConfig
+
+# ── Config ───────────────────────────────────────────────────────────
+MODEL_ID = "Qwen/Qwen2.5-Coder-3B-Instruct"
+DATASET_ID = "esmith5594/google-classroom-drive-api-code"
+OUTPUT_DIR = "qwen25-coder-3b-google-api-lora"
+HUB_MODEL_ID = "esmith5594/qwen25-coder-3b-google-api-lora"
+
+# ── Load Dataset ─────────────────────────────────────────────────────
+dataset = load_dataset(DATASET_ID, split="train")
+print(f"Loaded {len(dataset)} training examples")
+
+# ── LoRA Config (based on Octopus paper + TRL best practices) ─────────
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=[
+        "q_proj", "k_proj", "v_proj", "o_proj",
+        "gate_proj", "up_proj", "down_proj",
+    ],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+# ── Training Config ──────────────────────────────────────────────────
+training_args = SFTConfig(
+    output_dir=OUTPUT_DIR,
+    hub_model_id=HUB_MODEL_ID,
+    push_to_hub=True,
+    num_train_epochs=5,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=128,
+    learning_rate=2e-5,
+    lr_scheduler_type="constant",
+    warmup_ratio=0.0,
+    bf16=True,
+    gradient_checkpointing=True,
+    max_seq_length=4096,
+    logging_steps=10,
+    logging_first_step=True,
+    disable_tqdm=True,
+    save_strategy="epoch",
+    save_total_limit=2,
+    report_to="trackio",
+    run_name="qwen25-coder-3b-google-api-lora",
+    project="google-api-coder",
+    assistant_only_loss=True,
+    packing=False,
+)
+
+# ── Trainer ──────────────────────────────────────────────────────────
+trainer = SFTTrainer(
+    model=MODEL_ID,
+    train_dataset=dataset,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# ── Train ────────────────────────────────────────────────────────────
+trainer.train()
+
+# ── Save ─────────────────────────────────────────────────────────────
+trainer.save_model(os.path.join(OUTPUT_DIR, "final"))
+trainer.push_to_hub()
+
+print(f"\nTraining complete! Model saved to {HUB_MODEL_ID}")