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- 00576224/adapter_config.json +1 -0
- 00576224/adapter_model.bin +3 -0
- 00576224/config.json +1 -0
- 00576224/log_1730923439.txt +190 -0
- 00576224/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 00576224/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 009d5c81/adapter_config.json +1 -0
- 009d5c81/adapter_model.bin +3 -0
- 009d5c81/config.json +1 -0
- 009d5c81/log_1730923494.txt +250 -0
- 009d5c81/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 009d5c81/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 00dbd492/adapter_config.json +1 -0
- 00dbd492/adapter_model.bin +3 -0
- 00dbd492/config.json +1 -0
- 00dbd492/log_1730923997.txt +250 -0
- 00dbd492/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 00dbd492/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 03560426/adapter_config.json +1 -0
- 03560426/adapter_model.bin +3 -0
- 03560426/config.json +1 -0
- 03560426/log_1730924338.txt +250 -0
- 03560426/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 03560426/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 05a7bcf2/adapter_config.json +1 -0
- 05a7bcf2/adapter_model.bin +3 -0
- 05a7bcf2/config.json +1 -0
- 05a7bcf2/log_1730924590.txt +96 -0
- 05a7bcf2/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 05a7bcf2/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 0607ce86/adapter_config.json +1 -0
- 0607ce86/adapter_model.bin +3 -0
- 0607ce86/config.json +1 -0
- 0607ce86/log_1730924807.txt +144 -0
- 0607ce86/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 0607ce86/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 0692e18c/adapter_config.json +1 -0
- 0692e18c/adapter_model.bin +3 -0
- 0692e18c/config.json +1 -0
- 0692e18c/log_1730925029.txt +250 -0
- 0692e18c/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 0692e18c/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 070dd51e/adapter_config.json +1 -0
- 070dd51e/adapter_model.bin +3 -0
- 070dd51e/config.json +1 -0
- 070dd51e/log_1730925150.txt +72 -0
- 070dd51e/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +0 -0
- 070dd51e/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl +1 -0
- 08573cc6/adapter_config.json +1 -0
- 08573cc6/adapter_model.bin +3 -0
00576224/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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00576224/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:09a9243bac781e0b58f343a1e5b02a378d78f2686fc758ebb5de76f75f07bd5f
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size 596042770
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00576224/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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00576224/log_1730923439.txt
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00576224/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[9 9 7 7]\n [9 9 7 7]\n [5 5 9 9]\n [5 5 9 9]] -> [[9 9 7 7 5 5 9 9 9 9 7 7]\n [9 9 7 7 5 5 9 9 9 9 7 7]\n [5 5 9 9 9 9 7 7 5 5 9 9]\n [5 5 9 9 9 9 7 7 5 5 9 9]\n [9 9 7 7 5 5 9 9 9 9 7 7]\n [9 9 7 7 5 5 9 9 9 9 7 7]\n [5 5 9 9 9 9 7 7 5 5 9 9]\n [5 5 9 9 9 9 7 7 5 5 9 9]\n [9 9 7 7 5 5 9 9 9 9 7 7]\n [9 9 7 7 5 5 9 9 9 9 7 7]\n [5 5 9 9 9 9 7 7 5 5 9 9]\n [5 5 9 9 9 9 7 7 5 5 9 9]]#\n"}, {"role": "user", "content": "[[5 5 8 8]\n [5 5 8 8]\n [1 1 6 6]\n [1 1 6 6]] -> "}], "output": {"role": "assistant", "content": "[[5 5 8 8 1 1 6 6 5 5 8 8]\n [5 5 8 8 1 1 6 6 5 5 8 8]\n [1 1 6 6 5 5 8 8 1 1 6 6]\n [1 1 6 6 5 5 8 8 1 1 6 6]\n [5 5 8 8 1 1 6 6 5 5 8 8]\n [5 5 8 8 1 1 6 6 5 5 8 8]\n [1 1 6 6 5 5 8 8 1 1 6 6]\n [1 1 6 6 5 5 8 8 1 1 6 6]\n [5 5 8 8 1 1 6 6 5 5 8 8]\n [5 5 8 8 1 1 6 6 5 5 8 8]\n [1 1 6 6 5 5 8 8 1 1 6 6]\n [1 1 6 6 5 5 8 8 1 1 6 6]]#"}, "total_tokens": 735}
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009d5c81/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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009d5c81/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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009d5c81/config.json
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| 1 |
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
|
009d5c81/log_1730923494.txt
ADDED
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| 1 |
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| 2 |
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Step 232 | loss:0.00033673577127046883 lr:1.5708419435684462e-06 tokens_per_second_per_gpu:6945.267938724145
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Step 233 | loss:0.00025602109963074327 lr:1.4019499710726913e-06 tokens_per_second_per_gpu:6790.554445807879
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Step 234 | loss:0.00047656253445893526 lr:1.2425322847218368e-06 tokens_per_second_per_gpu:6771.395451788634
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Step 235 | loss:0.00046232191380113363 lr:1.0926199633097157e-06 tokens_per_second_per_gpu:6758.807390887128
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Step 236 | loss:0.0018393310019746423 lr:9.522422325404235e-07 tokens_per_second_per_gpu:6817.929599404819
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Step 237 | loss:0.0011434406042099 lr:8.214264593307098e-07 tokens_per_second_per_gpu:6823.818443994626
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Step 238 | loss:0.0012595762964338064 lr:7.001981464747565e-07 tokens_per_second_per_gpu:6804.744850083202
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Step 239 | loss:0.0027921022847294807 lr:5.885809276723608e-07 tokens_per_second_per_gpu:7088.774800407417
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Step 240 | loss:0.0010998341022059321 lr:4.865965629214819e-07 tokens_per_second_per_gpu:6783.880260917502
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Step 241 | loss:0.0020137943793088198 lr:3.9426493427611177e-07 tokens_per_second_per_gpu:7076.471847159209
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Step 242 | loss:0.0006402755388990045 lr:3.1160404197018154e-07 tokens_per_second_per_gpu:7070.256800749601
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Step 243 | loss:0.0020476654171943665 lr:2.386300009084408e-07 tokens_per_second_per_gpu:6931.143869152546
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Step 244 | loss:0.0016461233608424664 lr:1.753570375247815e-07 tokens_per_second_per_gpu:6873.38200128151
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Step 245 | loss:0.0015378650277853012 lr:1.2179748700879012e-07 tokens_per_second_per_gpu:6931.3903853247375
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Step 246 | loss:0.0012600197223946452 lr:7.796179090094891e-08 tokens_per_second_per_gpu:6836.136965216229
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Step 247 | loss:0.0010782753815874457 lr:4.385849505708084e-08 tokens_per_second_per_gpu:6771.003891387363
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Step 248 | loss:0.0006726465653628111 lr:1.949424798228239e-08 tokens_per_second_per_gpu:7068.659410114673
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Step 249 | loss:0.0013102944940328598 lr:4.873799534788059e-09 tokens_per_second_per_gpu:7036.706840644525
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Step 250 | loss:0.0007836272125132382 lr:0.0 tokens_per_second_per_gpu:6834.360445101376
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009d5c81/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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009d5c81/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0 0 0 0 0]\n [0 0 0 3 3 0 3 3 3 0 0 0 0 0]\n [0 0 0 0 3 3 0 3 0 0 0 0 0 0]\n [0 0 3 3 0 0 3 0 0 0 0 0 0 0]\n [0 0 3 0 3 3 0 0 0 0 0 0 0 0]\n [0 0 0 3 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 6 0 6 0 0]\n [0 0 0 0 0 0 0 0 0 0 6 0 0 0]\n [0 0 0 0 0 0 0 0 0 6 6 6 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 8 0 0 0 0 0 0 0 0]\n [0 0 0 8 8 0 8 8 8 0 0 0 0 0]\n [0 0 0 0 8 8 0 8 0 0 0 0 0 0]\n [0 0 8 8 0 0 8 0 0 0 0 0 0 0]\n [0 0 8 0 8 8 0 0 0 0 0 0 0 0]\n [0 0 0 8 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n[[0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 3 3 0 3 3 0 0 0 0 0 0 0]\n [0 0 0 3 3 3 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 3 3 3 3 0 0 0 0 0]\n [0 0 3 3 0 0 3 0 3 3 0 0 0 0]\n [0 0 0 3 3 3 3 0 0 0 0 0 0 0]\n [0 0 0 3 0 3 0 0 0 0 0 0 0 0]\n [0 3 3 3 0 3 3 3 0 0 0 0 0 0]\n [0 3 0 0 0 0 0 3 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 6 0 0]\n [0 0 0 0 0 0 0 0 0 0 6 6 6 0]\n [0 0 0 0 0 0 0 0 0 0 0 6 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 9 9 0 9 9 0 0 0 0 0 0 0]\n [0 0 0 9 9 9 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 9 9 9 9 0 0 0 0 0]\n [0 0 9 9 0 0 9 0 9 9 0 0 0 0]\n [0 0 0 9 9 9 9 0 0 0 0 0 0 0]\n [0 0 0 9 0 9 0 0 0 0 0 0 0 0]\n [0 9 9 9 0 9 9 9 0 0 0 0 0 0]\n [0 9 0 0 0 0 0 9 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n[[0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 3 3 0 3 0 3 3 0 0 0 0]\n [0 0 0 3 0 3 0 3 0 3 0 0 0 0]\n [0 0 0 3 3 0 3 0 3 3 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 0 6 0 0 0]\n [0 0 0 0 0 0 0 0 0 6 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 6 6 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 8 8 0 8 0 8 8 0 0 0 0]\n [0 0 0 8 0 8 0 8 0 8 0 0 0 0]\n [0 0 0 8 8 0 8 0 8 8 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n[[0 0 0 0 0 3 3 0 0 0 0 0 0 0]\n [0 0 0 3 3 3 0 0 0 0 0 0 0 0]\n [0 3 0 3 0 0 0 0 0 0 0 0 0 0]\n [0 3 3 3 0 3 3 3 3 0 0 0 0 0]\n [3 3 0 3 3 3 0 0 3 3 0 0 0 0]\n [0 3 0 0 0 3 3 0 0 0 0 0 0 0]\n [0 3 3 0 0 0 3 3 0 0 0 0 0 0]\n [0 0 3 3 3 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 6 6 6 0 0 0 0]\n [0 0 0 0 0 0 0 6 0 6 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 5 5 0 0 0 0 0 0 0]\n [0 0 0 5 5 5 0 0 0 0 0 0 0 0]\n [0 5 0 5 0 0 0 0 0 0 0 0 0 0]\n [0 5 5 5 0 5 5 5 5 0 0 0 0 0]\n [5 5 0 5 5 5 0 0 5 5 0 0 0 0]\n [0 5 0 0 0 5 5 0 0 0 0 0 0 0]\n [0 5 5 0 0 0 5 5 0 0 0 0 0 0]\n [0 0 5 5 5 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 0 0 0 0 0 3 0 0 0 3 0 0 0]\n [0 0 0 0 3 0 3 0 3 0 3 0 3 0]\n [0 0 0 0 3 3 3 3 3 3 3 3 3 0]\n [0 0 0 0 3 0 3 0 3 0 3 0 3 0]\n [0 0 0 0 3 0 0 0 3 0 0 0 3 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 6 6 6 0 0 0 0 0 0 0]\n [0 0 0 0 0 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> "}], "output": {"role": "assistant", "content": "[[0 0 0 0 0 0 9 0 0 0 9 0 0 0]\n [0 0 0 0 9 0 9 0 9 0 9 0 9 0]\n [0 0 0 0 9 9 9 9 9 9 9 9 9 0]\n [0 0 0 0 9 0 9 0 9 0 9 0 9 0]\n [0 0 0 0 9 0 0 0 9 0 0 0 9 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#"}, "total_tokens": 4129}
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00dbd492/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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00dbd492/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f27c8f3e53b3fe8a2c1b07344473faa831ac4d423b1d792d7203f59aa81b4aa6
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size 596042770
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00dbd492/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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00dbd492/log_1730923997.txt
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| 1 |
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Step 1 | loss:0.013372980058193207 lr:4.000000000000001e-06 tokens_per_second_per_gpu:3874.8165404626934
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| 2 |
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Step 2 | loss:0.008318759500980377 lr:8.000000000000001e-06 tokens_per_second_per_gpu:3994.9100071089683
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| 3 |
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Step 3 | loss:0.020588943734765053 lr:1.2e-05 tokens_per_second_per_gpu:2622.9347670806737
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| 4 |
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Step 4 | loss:0.015207692980766296 lr:1.6000000000000003e-05 tokens_per_second_per_gpu:7117.680403972301
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| 5 |
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Step 5 | loss:0.015772100538015366 lr:2e-05 tokens_per_second_per_gpu:2841.3650484667232
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Step 6 | loss:0.014235018752515316 lr:2.4e-05 tokens_per_second_per_gpu:7144.18862882571
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| 7 |
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Step 7 | loss:0.04776996001601219 lr:2.8000000000000003e-05 tokens_per_second_per_gpu:6944.746126975525
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| 8 |
+
Step 8 | loss:0.01470720861107111 lr:3.2000000000000005e-05 tokens_per_second_per_gpu:7099.559212838606
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| 9 |
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Step 9 | loss:0.011193573474884033 lr:3.6e-05 tokens_per_second_per_gpu:7085.40196576822
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| 10 |
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Step 10 | loss:0.010021017864346504 lr:4e-05 tokens_per_second_per_gpu:7123.146230649066
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| 11 |
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Step 11 | loss:0.01647418551146984 lr:4.4000000000000006e-05 tokens_per_second_per_gpu:7026.652050826819
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| 12 |
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Step 12 | loss:0.011999926529824734 lr:4.8e-05 tokens_per_second_per_gpu:7097.950716141434
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| 13 |
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Step 13 | loss:0.022550508379936218 lr:5.2000000000000004e-05 tokens_per_second_per_gpu:5038.530118848792
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| 14 |
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Step 14 | loss:0.01995103433728218 lr:5.6000000000000006e-05 tokens_per_second_per_gpu:4248.733461591392
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| 15 |
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Step 15 | loss:0.006620384287089109 lr:6e-05 tokens_per_second_per_gpu:7131.65967860037
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| 16 |
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Step 16 | loss:0.00740306731313467 lr:6.400000000000001e-05 tokens_per_second_per_gpu:7127.628243449412
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| 17 |
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Step 17 | loss:0.0060096438974142075 lr:6.800000000000001e-05 tokens_per_second_per_gpu:4974.283310307197
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| 18 |
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Step 18 | loss:0.006891552358865738 lr:7.2e-05 tokens_per_second_per_gpu:7093.440515229761
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| 19 |
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Step 19 | loss:0.03327202796936035 lr:7.6e-05 tokens_per_second_per_gpu:3179.4929076391018
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| 20 |
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Step 20 | loss:0.009643638506531715 lr:8e-05 tokens_per_second_per_gpu:3810.467892928971
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| 21 |
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Step 21 | loss:0.005601323209702969 lr:8.4e-05 tokens_per_second_per_gpu:3646.923119975776
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| 22 |
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Step 22 | loss:0.007782038766890764 lr:8.800000000000001e-05 tokens_per_second_per_gpu:7135.31195854449
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| 23 |
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Step 23 | loss:0.00458392733708024 lr:9.200000000000001e-05 tokens_per_second_per_gpu:7043.86940911673
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| 24 |
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Step 24 | loss:0.004309103358536959 lr:9.6e-05 tokens_per_second_per_gpu:4565.439191471691
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| 25 |
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Step 25 | loss:0.01496342197060585 lr:0.0001 tokens_per_second_per_gpu:7025.824794922582
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00dbd492/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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00dbd492/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [5 5 5 5 5 0 0 0 0 0 0 0 0 0 0]\n [5 0 0 0 5 0 0 0 0 0 0 0 0 0 0]\n [5 0 5 0 5 0 0 0 0 0 0 0 0 0 0]\n [5 0 0 0 5 0 0 0 0 0 0 0 0 0 0]\n [5 5 5 5 5 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 5 5 5 5 5 5 5 5 5]\n [0 0 0 0 0 0 5 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 0 0 0 5 0 0 0 5]\n [0 0 0 0 0 0 5 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 5 5 5 5 5 5 5 5]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [5 5 5 5 5 0 0 0 0 0 0 0 0 0 0]\n [5 1 1 1 5 0 0 0 0 0 0 0 0 0 0]\n [5 1 5 1 5 0 0 0 0 0 0 0 0 0 0]\n [5 1 1 1 5 0 0 0 0 0 0 0 0 0 0]\n [5 5 5 5 5 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 5 5 5 5 5 5 5 5 5]\n [0 0 0 0 0 0 5 6 6 6 6 6 6 6 5]\n [0 0 0 0 0 0 5 6 6 6 6 6 6 6 5]\n [0 0 0 0 0 0 5 6 6 6 6 6 6 6 5]\n [0 0 0 0 0 0 5 6 6 6 5 6 6 6 5]\n [0 0 0 0 0 0 5 6 6 6 6 6 6 6 5]\n [0 0 0 0 0 0 5 6 6 6 6 6 6 6 5]\n [0 0 0 0 0 0 5 6 6 6 6 6 6 6 5]\n [0 0 0 0 0 0 5 5 5 5 5 5 5 5 5]]#\n[[0 0 5 5 5 5 5]\n [0 0 5 0 0 0 5]\n [0 0 5 0 5 0 5]\n [0 0 5 0 0 0 5]\n [0 0 5 5 5 5 5]\n [0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0]] -> [[0 0 5 5 5 5 5]\n [0 0 5 1 1 1 5]\n [0 0 5 1 5 1 5]\n [0 0 5 1 1 1 5]\n [0 0 5 5 5 5 5]\n [0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0]]#\n[[0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 5 0 0 0 5]\n [0 0 0 0 0 0 0 0 5 0 5 0 5]\n [0 0 0 0 0 0 0 0 5 0 0 0 5]\n [5 5 5 5 5 5 5 0 5 5 5 5 5]\n [5 0 0 0 0 0 5 0 0 0 0 0 0]\n [5 0 0 0 0 0 5 0 0 0 0 0 0]\n [5 0 0 5 0 0 5 0 0 0 0 0 0]\n [5 0 0 0 0 0 5 0 0 0 0 0 0]\n [5 0 0 0 0 0 5 0 0 0 0 0 0]\n [5 5 5 5 5 5 5 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 5 1 1 1 5]\n [0 0 0 0 0 0 0 0 5 1 5 1 5]\n [0 0 0 0 0 0 0 0 5 1 1 1 5]\n [5 5 5 5 5 5 5 0 5 5 5 5 5]\n [5 9 9 9 9 9 5 0 0 0 0 0 0]\n [5 9 9 9 9 9 5 0 0 0 0 0 0]\n [5 9 9 5 9 9 5 0 0 0 0 0 0]\n [5 9 9 9 9 9 5 0 0 0 0 0 0]\n [5 9 9 9 9 9 5 0 0 0 0 0 0]\n [5 5 5 5 5 5 5 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 0 5 5 5 5 5 5 5]\n [0 0 5 0 0 0 0 0 5]\n [0 0 5 0 0 0 0 0 5]\n [0 0 5 0 0 5 0 0 5]\n [0 0 5 0 0 0 0 0 5]\n [0 0 5 0 0 0 0 0 5]\n [0 0 5 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0]] -> "}], "output": {"role": "assistant", "content": "[[0 0 5 5 5 5 5 5 5]\n [0 0 5 9 9 9 9 9 5]\n [0 0 5 9 9 9 9 9 5]\n [0 0 5 9 9 5 9 9 5]\n [0 0 5 9 9 9 9 9 5]\n [0 0 5 9 9 9 9 9 5]\n [0 0 5 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0]]#"}, "total_tokens": 2251}
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03560426/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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version https://git-lfs.github.com/spec/v1
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oid sha256:68d9f5021b82bfba90c24ad2434ed887038bcffa33da1e106575dfddea653cc6
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03560426/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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| 1 |
+
Step 1 | loss:0.09594444930553436 lr:4.000000000000001e-06 tokens_per_second_per_gpu:1715.6769661527067
|
| 2 |
+
Step 2 | loss:0.032612573355436325 lr:8.000000000000001e-06 tokens_per_second_per_gpu:4257.384016888243
|
| 3 |
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Step 3 | loss:0.03703868016600609 lr:1.2e-05 tokens_per_second_per_gpu:7118.213139384143
|
| 4 |
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Step 4 | loss:0.04869644343852997 lr:1.6000000000000003e-05 tokens_per_second_per_gpu:2861.776388430861
|
| 5 |
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Step 5 | loss:0.04404347762465477 lr:2e-05 tokens_per_second_per_gpu:2890.405749137227
|
| 6 |
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Step 6 | loss:0.04101202264428139 lr:2.4e-05 tokens_per_second_per_gpu:7102.234102434205
|
| 7 |
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Step 7 | loss:0.048599839210510254 lr:2.8000000000000003e-05 tokens_per_second_per_gpu:7020.581060628121
|
| 8 |
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Step 8 | loss:0.024283362552523613 lr:3.2000000000000005e-05 tokens_per_second_per_gpu:6993.611958028155
|
| 9 |
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Step 9 | loss:0.03192838281393051 lr:3.6e-05 tokens_per_second_per_gpu:7122.84532645759
|
| 10 |
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Step 10 | loss:0.07498080283403397 lr:4e-05 tokens_per_second_per_gpu:6758.487752244918
|
| 11 |
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Step 11 | loss:0.05588093400001526 lr:4.4000000000000006e-05 tokens_per_second_per_gpu:2187.1191955052795
|
| 12 |
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Step 12 | loss:0.06042847782373428 lr:4.8e-05 tokens_per_second_per_gpu:7091.115951473602
|
| 13 |
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Step 13 | loss:0.07163452357053757 lr:5.2000000000000004e-05 tokens_per_second_per_gpu:6771.857666986925
|
| 14 |
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Step 14 | loss:0.020770439878106117 lr:5.6000000000000006e-05 tokens_per_second_per_gpu:3408.1900982120937
|
| 15 |
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Step 15 | loss:0.07795064151287079 lr:6e-05 tokens_per_second_per_gpu:6720.726208902146
|
| 16 |
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Step 16 | loss:0.049128226935863495 lr:6.400000000000001e-05 tokens_per_second_per_gpu:7154.987550845856
|
| 17 |
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Step 17 | loss:0.03991858661174774 lr:6.800000000000001e-05 tokens_per_second_per_gpu:7038.071954264581
|
| 18 |
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|
| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 36 |
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| 38 |
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| 39 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 56 |
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| 57 |
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| 58 |
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| 60 |
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| 66 |
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03560426/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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03560426/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4]\n [0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4]\n [0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4]\n [0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4]\n [0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4]\n [0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]] -> [[4 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0]\n [4 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0]\n [4 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0]\n [4 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0]\n [4 4 4 4 4 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n [4 4 4 4 4 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 1 1 6 6 6 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 1 1 6 6 6 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 6 6 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 6 6 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 6 6 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 6 6 6 6 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 6 6 6 6 6 6 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 6 6 6 6 6 6 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5]] -> [[8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 6 6 6 6 6 6 6 6 5 5 5 5 5 5 0 0 0 0]\n [8 8 6 6 6 6 6 6 6 6 5 5 5 5 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7 7]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7 7]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7 7]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7 7]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 6]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8 8 8 8 8]] -> "}], "output": {"role": "assistant", "content": "[[7 7 7 7 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [7 7 7 7 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [7 7 7 7 6 6 6 6 0 0 0 0 0 0 0 0 0 0 0 0]\n [7 7 7 7 6 6 6 6 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 6 6 5 5 5 5 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 6 6 5 5 5 5 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 5 5 8 8 8 8 8 8 8 8 0 0 0 0]\n [0 0 0 0 0 0 5 5 8 8 8 8 8 8 8 8 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#"}, "total_tokens": 4985}
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05a7bcf2/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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version https://git-lfs.github.com/spec/v1
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oid sha256:73ea37bff6d800eb8d009e71edae1bf66e2508be90518c96f59ef05097643a12
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05a7bcf2/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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05a7bcf2/log_1730924590.txt
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Step 1 | loss:0.05179863050580025 lr:1.1111111111111112e-05 tokens_per_second_per_gpu:4860.945113786926
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Step 2 | loss:0.0376623161137104 lr:2.2222222222222223e-05 tokens_per_second_per_gpu:6834.301743985016
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Step 3 | loss:0.0394112728536129 lr:3.3333333333333335e-05 tokens_per_second_per_gpu:6792.444651679777
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Step 5 | loss:0.051123712211847305 lr:5.555555555555556e-05 tokens_per_second_per_gpu:6802.744032109486
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Step 6 | loss:0.03351309150457382 lr:6.666666666666667e-05 tokens_per_second_per_gpu:6826.979193318932
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Step 7 | loss:0.061334047466516495 lr:7.777777777777778e-05 tokens_per_second_per_gpu:6821.014173507077
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Step 8 | loss:0.01792784593999386 lr:8.888888888888889e-05 tokens_per_second_per_gpu:6834.16073598883
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Step 15 | loss:0.010380257852375507 lr:9.883102778550434e-05 tokens_per_second_per_gpu:6801.825966163852
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Step 16 | loss:0.03979611024260521 lr:9.841114703012817e-05 tokens_per_second_per_gpu:6812.3183912732675
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Step 27 | loss:0.005111854523420334 lr:8.980465328528219e-05 tokens_per_second_per_gpu:6811.4291322974195
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Step 28 | loss:0.00833337102085352 lr:8.868629196864182e-05 tokens_per_second_per_gpu:6792.721061225793
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Step 29 | loss:0.004015392158180475 lr:8.751749110782012e-05 tokens_per_second_per_gpu:6802.514837515657
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Step 30 | loss:0.027002353221178055 lr:8.629977459615655e-05 tokens_per_second_per_gpu:6815.317941212828
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Step 31 | loss:0.004000282846391201 lr:8.503473010366713e-05 tokens_per_second_per_gpu:6819.934152465108
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Step 32 | loss:0.008173882961273193 lr:8.37240070070257e-05 tokens_per_second_per_gpu:6803.155938800446
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Step 33 | loss:0.012675347737967968 lr:8.236931423909138e-05 tokens_per_second_per_gpu:6816.343440706613
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Step 34 | loss:0.006324874237179756 lr:8.097241806078615e-05 tokens_per_second_per_gpu:6809.894525951748
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Step 35 | loss:0.005416011903434992 lr:7.953513975822755e-05 tokens_per_second_per_gpu:6808.5298231387305
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Step 44 | loss:0.004058449529111385 lr:6.510702077847863e-05 tokens_per_second_per_gpu:6809.4810600403825
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Step 45 | loss:0.002956643933430314 lr:6.337641692646106e-05 tokens_per_second_per_gpu:6816.696084771359
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Step 46 | loss:0.003887909697368741 lr:6.162837277871553e-05 tokens_per_second_per_gpu:6786.40257028325
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Step 47 | loss:0.014375696890056133 lr:5.9865167451320005e-05 tokens_per_second_per_gpu:6811.3195278545345
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Step 48 | loss:0.0021371471229940653 lr:5.808909982763825e-05 tokens_per_second_per_gpu:6810.95771134
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Step 49 | loss:0.002866639057174325 lr:5.6302485561014475e-05 tokens_per_second_per_gpu:6819.357207569117
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Step 50 | loss:0.0011557599063962698 lr:5.4507654055603275e-05 tokens_per_second_per_gpu:6805.72853194876
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Step 51 | loss:0.018013911321759224 lr:5.270694542927088e-05 tokens_per_second_per_gpu:6809.846715099813
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Step 52 | loss:0.0026188462506979704 lr:5.090270746252802e-05 tokens_per_second_per_gpu:6800.943318320658
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Step 53 | loss:0.011280328966677189 lr:4.909729253747197e-05 tokens_per_second_per_gpu:6813.292069306693
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05a7bcf2/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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05a7bcf2/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n [0 0 2 0 0 2 2 2 0 0 2 0 2 0 0 0 2 2 0 0 2 0 2 0 0 2 2 0 0 2]\n [0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 4 4 4 0 0 0 0 0 0 4 4 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 4 4 4 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 2 2 2 0 0 0 2 2 2 0 0 0 0 0 0 2 2 0 0 0 0 2 2 0 0 0]\n [0 0 0 0 8 2 2 0 0 0 2 8 2 0 0 0 0 0 0 8 2 0 0 0 0 2 2 0 0 0]\n [0 0 0 0 8 8 2 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [2 2 2 2 8 8 8 2 2 2 8 8 8 2 2 2 2 2 2 8 8 2 2 2 2 8 8 2 2 2]\n [0 0 2 0 8 8 8 2 0 0 8 8 8 0 0 0 2 2 0 8 8 0 2 0 0 8 8 0 0 2]\n [0 0 0 0 8 8 8 2 0 0 8 8 8 0 0 0 2 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [0 0 0 0 8 8 8 0 0 0 8 8 8 0 0 0 0 0 0 8 8 0 0 0 0 8 8 0 0 0]\n [8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8]\n [0 0 0 0 4 4 4 0 0 0 4 4 4 0 0 0 0 0 0 4 4 0 0 0 0 4 4 0 0 0]\n [0 0 0 0 4 4 4 0 0 0 4 4 4 0 0 0 0 0 0 4 4 0 0 0 0 4 4 0 0 0]\n [0 0 0 0 3 3 3 0 0 0 4 4 4 0 0 0 0 0 0 4 4 0 0 0 0 4 4 0 0 0]\n [0 0 0 0 3 3 0 0 0 0 4 4 4 0 0 0 0 0 0 4 4 0 0 0 0 4 4 0 0 0]\n [0 0 0 0 3 3 0 0 0 0 4 4 4 0 0 0 0 0 0 4 4 0 0 0 0 3 3 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 3 3 3 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 3 3 3 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 4 4 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 4 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 4 4 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 4 4 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 4 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 4 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 2 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 4 4 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 4 4 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]] -> "}], "output": {"role": "assistant", "content": "[[0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [2 2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 4 3 3 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [2 2 2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 3 0 0 0 0 0]\n [2 2 2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 3 3 0 0 0 0]\n [2 2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 3 3 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [2 2 2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 4 4 4 3 0 0]\n [2 2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 4 4 4 3 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 2 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 4 4 3 3 0 0]\n [2 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 4 4 3 3 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0]]#"}, "total_tokens": 7383}
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0607ce86/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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version https://git-lfs.github.com/spec/v1
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oid sha256:ccdd2dd61451a98b935288c9cbc2b0a269411dceb212f210425c9f8c1c0899b2
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0607ce86/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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Step 123 | loss:1.290653131036379e-06 lr:6.301598368674105e-06 tokens_per_second_per_gpu:6961.8538852270485
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| 124 |
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Step 124 | loss:0.0001666852185735479 lr:5.727198717339511e-06 tokens_per_second_per_gpu:6964.692810445289
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| 125 |
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Step 125 | loss:1.8864420781028457e-07 lr:5.178653135214812e-06 tokens_per_second_per_gpu:7084.47222098494
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| 126 |
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Step 126 | loss:1.4434226613957435e-06 lr:4.65628195747273e-06 tokens_per_second_per_gpu:6965.933476281354
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| 127 |
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Step 127 | loss:0.0004926001420244575 lr:4.16039023417088e-06 tokens_per_second_per_gpu:7070.743126358531
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| 128 |
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Step 128 | loss:6.65172024127969e-07 lr:3.691267552111183e-06 tokens_per_second_per_gpu:7058.634358221531
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| 129 |
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Step 129 | loss:2.423998375888914e-05 lr:3.249187865729264e-06 tokens_per_second_per_gpu:6960.316882297749
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| 130 |
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Step 130 | loss:2.3096629320207285e-06 lr:2.8344093371128424e-06 tokens_per_second_per_gpu:6967.707629269878
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| 131 |
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Step 131 | loss:8.259024184553709e-07 lr:2.4471741852423237e-06 tokens_per_second_per_gpu:7091.881830761651
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| 132 |
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Step 132 | loss:0.00036341266240924597 lr:2.087708544541689e-06 tokens_per_second_per_gpu:7028.734700444715
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| 133 |
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Step 133 | loss:5.048845196142793e-05 lr:1.7562223328224325e-06 tokens_per_second_per_gpu:7054.698139756758
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| 134 |
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Step 134 | loss:1.331735461462813e-06 lr:1.4529091286973995e-06 tokens_per_second_per_gpu:6891.879518942568
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| 135 |
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Step 135 | loss:0.00016570062143728137 lr:1.1779460585363944e-06 tokens_per_second_per_gpu:6894.350001130334
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| 136 |
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Step 136 | loss:8.915412763599306e-07 lr:9.314936930293283e-07 tokens_per_second_per_gpu:6967.057064696187
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| 137 |
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Step 137 | loss:3.679159135572263e-06 lr:7.136959534174592e-07 tokens_per_second_per_gpu:6951.214306958658
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| 138 |
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Step 138 | loss:1.686560040070617e-06 lr:5.246800274474439e-07 tokens_per_second_per_gpu:7062.8543555851875
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| 139 |
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Step 139 | loss:2.881310194879916e-07 lr:3.6455629509730136e-07 tokens_per_second_per_gpu:7034.988814681111
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| 140 |
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Step 140 | loss:1.2103055269108154e-06 lr:2.334182641175686e-07 tokens_per_second_per_gpu:6775.31819688721
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| 141 |
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Step 141 | loss:8.536469249520451e-05 lr:1.3134251542544774e-07 tokens_per_second_per_gpu:6948.348891929506
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| 142 |
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Step 142 | loss:1.8825789993570652e-07 lr:5.838865838366792e-08 tokens_per_second_per_gpu:7050.198371432318
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Step 143 | loss:4.7263534952435293e-07 lr:1.4599295990352924e-08 tokens_per_second_per_gpu:7049.281042448092
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| 144 |
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Step 144 | loss:8.21501889731735e-05 lr:0.0 tokens_per_second_per_gpu:7089.175553410856
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0607ce86/td_False_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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0607ce86/td_True_ttd_False_ttdwa_False_ad_True_trd_False.jsonl
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 0 0 0 0 0 0]\n [0 0 6 6 6 6 6 6 0 0 6 6 6 6 3 6 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 3 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 3 0 0 0]\n [0 3 8 8 3 3 8 8 0 0 8 8 3 3 8 3 0 0 0 3 0 0]\n [0 3 8 8 3 3 8 8 0 0 8 8 3 3 8 8 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0]\n [0 3 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 6 6 6 6 6 6 0 0 6 6 3 6 6 6 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 3 8 3 3 8 3 0 3 0 0 0 3]\n [0 0 8 8 3 3 3 8 0 0 3 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 3 3 3 3 8 0 0 0 0 0 0]\n [3 3 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 0 0 0]\n [0 0 0 0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0]\n [0 0 6 6 3 6 6 6 0 3 6 6 6 3 6 6 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 3 0]\n [0 0 8 3 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 3 0 0 3 0 0 0 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 6 6 6 6 6 6 0 0 6 6 6 6 6 6 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 6 6 6 6 6 6 0 0 6 6 6 6 6 6 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 6 6 6 6 6 6 0 0 6 6 6 6 6 6 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 8 8 3 3 8 8 0 0 8 8 3 3 8 8 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n[[0 3 0 0 0 3 3 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 1 1 1 2 3 3 0 3 0 0]\n [0 1 1 3 3 3 0 1 1 2 3 3 0 1 1 2 3 3 3 0 3 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 1 3 3 0 0 0 3]\n [0 1 3 3 3 1 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 8 8 8 8 8 0 8 8 8 8 8 0 8 8 8 8 8 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 3 1 3 3 3 0 3 1 2 3 3 0 1 1 2 3 3 0 0 3 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 3 2 1 3 0 1 1 2 3 3 0 0 0 0]\n [1 1 1 2 3 3 0 1 1 2 3 3 3 1 3 2 3 3 0 0 0 0]\n [0 8 1 8 8 3 0 8 8 8 8 8 0 1 8 8 8 8 0 0 0 0]\n [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 3 0]\n [0 1 1 3 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 1 3 3 0 1 1 2 3 1 0 0 0 0]\n [1 1 1 2 3 3 0 1 1 2 3 1 0 1 1 2 3 3 0 0 0 0]\n [3 8 8 8 3 3 1 8 8 8 8 8 0 8 8 8 8 8 0 0 1 0]\n [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 3 3 0 3 0 3 0 1 1 0 3 0 0 0 0 0 0 0 0 0 1]\n [0 0 3 0 0 1 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 8 8 8 8 8 0 8 8 8 8 8 0 8 8 8 8 8 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 8 8 8 8 8 0 8 8 8 8 8 0 8 8 8 8 8 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 1 1 2 3 3 0 1 1 2 3 3 0 1 1 2 3 3 0 0 0 0]\n [0 8 8 8 8 8 0 8 8 8 8 8 0 8 8 8 8 8 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3]\n [0 2 2 2 2 2 0 2 2 2 2 0 0 2 2 2 2 0 0 0 0 3]\n [0 3 2 2 3 0 0 3 2 2 3 0 3 3 2 2 3 0 0 3 0 0]\n [0 2 3 3 3 0 0 3 3 3 3 0 0 3 3 3 3 0 0 0 0 0]\n [0 2 3 1 3 0 0 2 2 1 2 0 0 3 1 1 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0]\n [0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0]\n [0 2 2 2 2 3 0 2 2 2 2 0 0 2 2 2 2 2 0 0 0 0]\n [0 3 2 2 3 0 0 3 2 2 3 0 3 3 2 2 3 2 0 0 0 0]\n [0 3 3 3 3 0 0 3 3 3 3 0 0 3 3 3 3 0 0 0 0 0]\n [0 3 3 1 3 0 0 3 1 1 3 0 0 3 1 1 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0]\n [0 2 2 2 2 0 0 3 2 3 2 0 0 2 3 2 2 0 0 0 0 0]\n [0 3 2 2 3 0 0 3 2 3 3 0 0 3 2 3 3 0 0 0 0 0]\n [3 3 3 3 3 0 0 3 3 3 3 0 0 3 2 3 3 0 0 2 0 0]\n [0 3 1 1 3 0 3 3 1 1 3 0 0 3 1 1 3 0 0 0 0 0]\n [0 2 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 3 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 2 0 0 0 0 2 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 2 0 0]\n [0 0 0 3 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> "}], "output": {"role": "assistant", "content": "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 2 2 2 2 0 0 2 2 2 2 0 0 2 2 2 2 0 0 0 0 0]\n [0 3 2 2 3 0 0 3 2 2 3 0 0 3 2 2 3 0 0 0 0 0]\n [0 3 3 3 3 0 0 3 3 3 3 0 0 3 3 3 3 0 0 0 0 0]\n [0 3 1 1 3 0 0 3 1 1 3 0 0 3 1 1 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 2 2 2 2 0 0 2 2 2 2 0 0 2 2 2 2 0 0 0 0 0]\n [0 3 2 2 3 0 0 3 2 2 3 0 0 3 2 2 3 0 0 0 0 0]\n [0 3 3 3 3 0 0 3 3 3 3 0 0 3 3 3 3 0 0 0 0 0]\n [0 3 1 1 3 0 0 3 1 1 3 0 0 3 1 1 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 2 2 2 2 0 0 2 2 2 2 0 0 2 2 2 2 0 0 0 0 0]\n [0 3 2 2 3 0 0 3 2 2 3 0 0 3 2 2 3 0 0 0 0 0]\n [0 3 3 3 3 0 0 3 3 3 3 0 0 3 3 3 3 0 0 0 0 0]\n [0 3 1 1 3 0 0 3 1 1 3 0 0 3 1 1 3 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#"}, "total_tokens": 6005}
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0692e18c/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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0692e18c/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:073b6d5f936685ac2736e3c1a67af872075b1166f04665ea29d120447d7acbaa
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size 596042770
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0692e18c/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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0692e18c/log_1730925029.txt
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| 1 |
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Step 1 | loss:0.05883090943098068 lr:4.000000000000001e-06 tokens_per_second_per_gpu:2611.2751764532522
|
| 2 |
+
Step 2 | loss:0.1065659150481224 lr:8.000000000000001e-06 tokens_per_second_per_gpu:934.6274612544344
|
| 3 |
+
Step 3 | loss:0.10369853675365448 lr:1.2e-05 tokens_per_second_per_gpu:1633.880920446792
|
| 4 |
+
Step 4 | loss:0.06993220746517181 lr:1.6000000000000003e-05 tokens_per_second_per_gpu:2003.4908633730493
|
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[5 5 0 5 5 0]\n [0 0 5 0 0 5]\n [0 0 5 0 0 5]\n [5 5 0 5 5 0]\n [0 0 5 0 0 5]\n [0 0 5 0 0 5]] -> [[0 0 5 0 0 5 0 0 0 0 0 5 0 0 5 0 0 0]\n [5 5 0 5 5 0 0 0 0 5 5 0 5 5 0 0 0 0]\n [5 5 0 5 5 0 0 0 0 5 5 0 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 5 0 0 5 0 0 0 0 0 5 0 0 5 0 0 0]\n [5 5 0 5 5 0 0 0 0 5 5 0 5 5 0 0 0 0]\n [5 5 0 5 5 0 0 0 0 5 5 0 5 5 0 0 0 0]\n [0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 5]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]\n [0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 5 5 0]]#\n[[0 0 3 0 0 3]\n [0 3 0 0 3 0]\n [3 0 0 3 0 0]\n [0 0 3 0 0 3]\n [0 3 0 0 3 0]\n [3 0 0 3 0 0]] -> [[0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 3 3 0]\n [0 0 0 0 0 0 3 0 3 0 0 0 0 0 0 3 0 3]\n [0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 3 3]\n [0 0 0 3 3 0 0 0 0 0 0 0 3 3 0 0 0 0]\n [0 0 0 3 0 3 0 0 0 0 0 0 3 0 3 0 0 0]\n [0 0 0 0 3 3 0 0 0 0 0 0 0 3 3 0 0 0]\n [3 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0]\n [3 0 3 0 0 0 0 0 0 3 0 3 0 0 0 0 0 0]\n [0 3 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0]\n [0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 3 3 0]\n [0 0 0 0 0 0 3 0 3 0 0 0 0 0 0 3 0 3]\n [0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 3 3]\n [0 0 0 3 3 0 0 0 0 0 0 0 3 3 0 0 0 0]\n [0 0 0 3 0 3 0 0 0 0 0 0 3 0 3 0 0 0]\n [0 0 0 0 3 3 0 0 0 0 0 0 0 3 3 0 0 0]\n [3 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0]\n [3 0 3 0 0 0 0 0 0 3 0 3 0 0 0 0 0 0]\n [0 3 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 9 0 0 9 0]\n [9 9 9 9 9 9]\n [0 9 0 0 9 0]\n [0 9 0 0 9 0]\n [9 9 9 9 9 9]\n [0 9 0 0 9 0]] -> "}], "output": {"role": "assistant", "content": "[[0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [9 0 9 9 0 9 9 0 9 9 0 9 9 0 9 9 0 9]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [9 0 9 9 0 9 9 0 9 9 0 9 9 0 9 9 0 9]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [9 0 9 9 0 9 9 0 9 9 0 9 9 0 9 9 0 9]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [9 0 9 9 0 9 9 0 9 9 0 9 9 0 9 9 0 9]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 9 0 9 0 0 0 0 0 0 9 0 9 0 0 0]]#"}, "total_tokens": 2297}
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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version https://git-lfs.github.com/spec/v1
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oid sha256:3dfb333dd397963ba7e73a073b7d4d6b8868ef3493cc1391b850b995fe21ebd8
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070dd51e/config.json
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{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.40.0.dev0", "use_cache": true, "vocab_size": 128256}
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Step 1 | loss:0.01213599182665348 lr:1.4285714285714285e-05 tokens_per_second_per_gpu:3404.260116401829
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{"input": [{"role": "system", "content": "Figure out the underlying transformation in the following examples and apply it to the test case. Here are some examples from this transformation, your answer must follow the format.\nThe input-output grids are provided as python arrays:\n[[0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 6 0 0 0 0 6 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 7 0 0 7 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 9 0 0 0 0 9 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]] -> [[0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 6 6 6 6 3 6 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 3 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 7 2 7 7 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 9 2 9 9 9 9 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 2 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0]]#\n"}, {"role": "user", "content": "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 5 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 9 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]] -> "}], "output": {"role": "assistant", "content": "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 5 5 8 5 5 5 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 6 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 9 9 9 4 9 9 9 9 9 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]#"}, "total_tokens": 3363}
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08573cc6/adapter_config.json
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{"base_model_name_or_path": "/raid/lingo/akyurek/git/agi/checkpoints/pretrained/8B_grids_no_lm_generated_tasks/4k/", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "lora_alpha": 16.0, "lora_dropout": 0.0, "modules_to_save": null, "peft_type": "LORA", "r": 128, "target_modules": ["gate_proj", "down_proj", "up_proj", "lm_head", "q_proj", "v_proj"], "task_type": "CAUSAL_LM"}
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08573cc6/adapter_model.bin
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