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Starting run with config: mario/grid_search_path_gemma.yaml
Rodando
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!
Loaded 17071 levels from evaluation dataset
Starting grid search...
===== Processing Split: str_window_vertical_newline =====
===== Processing Subset: path_snake =====
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Dataset Loaded for str_window_vertical_newline - Subset: path_snake
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--- Processing Model Family: gemma3 ---
- Model: unsloth/gemma-3-12b-it-bnb-4bit
- LoRA Pair: r=128, alpha=128
- Other Params: {'num_train_epochs': 1, 'train_on_responses_only': True}
Run Name: mario_sft_dataset_numbers_v2.1_str_window_vertical_newline-gemma-3-12b-it--resp-r128-a128-e1-1353
==((====))== Unsloth 2025.4.7: Fast Gemma3 patching. Transformers: 4.51.3.
\\ /| NVIDIA H100 80GB HBM3. Num GPUs = 1. Max memory: 79.189 GB. Platform: Linux.
O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 9.0. CUDA Toolkit: 12.4. Triton: 3.2.0
\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False]
"-____-" Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
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Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
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GEMMA3 DEBUG - Standardized dataset first item: {'conversations': [{'content': 'I want a level that has 0 special enemies, 0 powerups, 0 goombas, 1 koopas, 30 ground blocks, 2 pipes, hard difficulty, overworld level, 14 breakable blocks, 0 coins, 4 hard blocks, 0 coin blocks, high elevation', 'role': 'user'}, {'content': 'XXP-------------\n-------------PXX\nXXP-------------\n-------------PXX\nXXPP------------\n----------PP--XX\nXX---P----------\n----------PP<[XX\nXX]>P-----------\n-----------PP-XX\nXXP-------------\n-------------PXX\n---PPP----------\n---------PP-----\n----SPP---------\n----------PS----\n----SPPP--------\n-------P--------\n---------P------\n------PS--------\n--------SP------\n------PS--------\n----------PP----\n---P------------\n--------R---P---\n---PP-----------\n-----S---PP-----\n-------PPPS-----\n-----SP--S------\n------S--PS-----\n-----SP--S------\n--------PP------\n--------PP------\n------P---------\n----------P-----\n-----P----------\n---------P------\n-------P--------\n-------PP-------\n---------PP-----\nXX--P-----------\n------------PPXX\nXXPPPP----------\n---------PP--P--\n----#P----------\n-------PPPP#----\n[[[[[[[(PP------\n-------P)]]]]]]]\n----#PPPP-------\n---------PP#----', 'role': 'assistant'}]}
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==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1
\\ /| Num examples = 17,071 | Num Epochs = 1 | Total steps = 2,134
O^O/ \_/ \ Batch size per device = 2 | Gradient accumulation steps = 4
\ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8
"-____-" Trainable parameters = 523,763,712/12,000,000,000 (4.36% trained)
Starting training for run: mario_sft_dataset_numbers_v2.1_str_window_vertical_newline-gemma-3-12b-it--resp-r128-a128-e1-1353
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Unsloth: Will smartly offload gradients to save VRAM!
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10%|▉ | 208/2134 [34:28<5:05:58, 9.53s/it]
10%|▉ | 208/2134 [34:28<5:05:58, 9.53s/it]
10%|▉ | 209/2134 [34:31<4:03:57, 7.60s/it]
10%|▉ | 209/2134 [34:31<4:03:57, 7.60s/it] |