feat(training): scale dataset to 500 prompts and add hub export cell for full hackathon training run

training/colab_train_v2.py

@@ -93,14 +93,14 @@ def make_prompt(bug):
         f"Fix the function:"
     )
 
-# Extremely fast trial (10 items)
+# Full hackathon training dataset (500 items to fit in 4hr Colab window)
 prompts = []
-for i in range(10):
+for i in range(500):
     bug = BUG_DATASET[i % len(BUG_DATASET)]
     prompts.append({"prompt": make_prompt(bug), "bug_index": i % len(BUG_DATASET)})
 
 dataset = Dataset.from_list(prompts)
-print(f"✅ Trial Dataset loaded: {len(dataset)} items")
+print(f"✅ Full Dataset loaded: {len(dataset)} items")
 
 
 # ============================================================
@@ -221,3 +221,13 @@ trainer = GRPOTrainer(
 print("🚀 Sparking up Unsloth GRPO Engine...")
 trainer.train()
 print("\n✅ UNSLOTH TRIAL TRAINING COMPLETE! Model is completely viable.")
+
+# ============================================================
+# CELL 6: Push Final Model Adapters to HF Hub
+# ============================================================
+# NOTE: Run this cell separately AFTER training completes.
+# You must have authorized HuggingFace via logging in (huggingface-cli login)
+#
+# model.push_to_hub("your-username/codesensei-unsloth-qwen3-1.7b", token="your_hf_token")
+# tokenizer.push_to_hub("your-username/codesensei-unsloth-qwen3-1.7b", token="your_hf_token")
+# print("✅ Successfully pushed Unsloth LoRA adapters to Hub!")