init

checkpoint-4000/config.json

@@ -1,26 +0,0 @@
-{
-  "_name_or_path": "/data/searchgpt/yq/GoGPT/outputs-pt-v1-7b-llama2/ckpt",
-  "architectures": [
-    "LlamaForCausalLM"
-  ],
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_act": "silu",
-  "hidden_size": 4096,
-  "initializer_range": 0.02,
-  "intermediate_size": 11008,
-  "max_position_embeddings": 2048,
-  "model_type": "llama",
-  "num_attention_heads": 32,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 32,
-  "pad_token_id": 0,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-05,
-  "rope_scaling": null,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float16",
-  "transformers_version": "4.29.1",
-  "use_cache": false,
-  "vocab_size": 68419
-}

checkpoint-4000/global_step4000/mp_rank_00_model_states.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:814e8129fe526c42fdbd6fe4ff2c9b21f0c161f6982e3883c3e5fea1260a4cb1
-size 2607759360

checkpoint-4000/global_step4000/zero_pp_rank_2_mp_rank_00_optim_states.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aabd69e33896e791496a8b91b6325d7e6dafe50483e2ed374bf68f6bbc025924
-size 840736473

checkpoint-4000/pytorch_model-00001-of-00002.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bcb73d2c6cc7cec148b9ea0157637718f3414f3a18d493daf4255e47befedae9
-size 10531361877

checkpoint-4000/rng_state_1.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f7f1e51f8b260af2143aa0e342ab171171f7d983fbaaf95e9400d872b4d8c542
-size 21687

checkpoint-4000/rng_state_2.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:057591ad3bc33c9f75b1e6556c3c29b4328e0785d67a420f2a5a8bb25e58812e
-size 21687

checkpoint-4000/rng_state_3.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5b595d89f2fe53ca5dccc290b880b858a60eb07a5f694175f05519f2257fafbf
-size 21687

checkpoint-4000/rng_state_4.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8dae996da909b80faa4e9fef9f945126f53fa3c7c6da06367a3f71ae24b0f138
-size 21687

checkpoint-4000/rng_state_5.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8de190b28a07218f4d05a1c7ccfccc4e53d6033af244fe3bd9fe3e20275d59de
-size 21687

checkpoint-4000/rng_state_6.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a4cccb61a1758c3ec09da0ddc7ef0957d32362d5baa800f340839c1fa72376e2
-size 21687

checkpoint-4000/rng_state_7.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ed085eb5cf063033df209f9c7714e3c87ee78a57b9ee629a7258a15bd8cde7ac
-size 21687

checkpoint-4000/trainer_state.json

@@ -1,3142 +0,0 @@
-{
-  "best_metric": null,
-  "best_model_checkpoint": null,
-  "epoch": 0.8226221079691517,
-  "global_step": 4000,
-  "is_hyper_param_search": false,
-  "is_local_process_zero": true,
-  "is_world_process_zero": true,
-  "log_history": [
-    {
-      "epoch": 0.0,
-      "learning_rate": 0,
-      "loss": 11.981,
-      "step": 1
-    },
-    {
-      "epoch": 0.0,
-      "learning_rate": 3.9970117109141705e-05,
-      "loss": 12.0789,
-      "step": 10
-    },
-    {
-      "epoch": 0.0,
-      "learning_rate": 9.331893267009234e-05,
-      "loss": 10.7133,
-      "step": 20
-    },
-    {
-      "epoch": 0.01,
-      "learning_rate": 0.00011407670594843083,
-      "loss": 8.7339,
-      "step": 30
-    },
-    {
-      "epoch": 0.01,
-      "learning_rate": 0.00012721122651399258,
-      "loss": 8.301,
-      "step": 40
-    },
-    {
-      "epoch": 0.01,
-      "learning_rate": 0.00013684136855727938,
-      "loss": 8.1964,
-      "step": 50
-    },
-    {
-      "epoch": 0.01,
-      "eval_accuracy": 0.10310832025117739,
-      "eval_loss": 8.203125,
-      "eval_runtime": 6.5764,
-      "eval_samples_per_second": 5.322,
-      "eval_steps_per_second": 0.304,
-      "step": 50
-    },
-    {
-      "epoch": 0.01,
-      "learning_rate": 0.00014444862339428802,
-      "loss": 8.0553,
-      "step": 60
-    },
-    {
-      "epoch": 0.01,
-      "learning_rate": 0.00015073705430110066,
-      "loss": 7.9436,
-      "step": 70
-    },
-    {
-      "epoch": 0.02,
-      "learning_rate": 0.00015609707636042195,
-      "loss": 7.8368,
-      "step": 80
-    },
-    {
-      "epoch": 0.02,
-      "learning_rate": 0.00016076788727202945,
-      "loss": 7.7333,
-      "step": 90
-    },
-    {
-      "epoch": 0.02,
-      "learning_rate": 0.00016490670495758757,
-      "loss": 7.6139,
-      "step": 100
-    },
-    {
-      "epoch": 0.02,
-      "eval_accuracy": 0.12459794174079888,
-      "eval_loss": 7.81640625,
-      "eval_runtime": 6.6059,
-      "eval_samples_per_second": 5.298,
-      "eval_steps_per_second": 0.303,
-      "step": 100
-    },
-    {
-      "epoch": 0.02,
-      "learning_rate": 0.0001686224178807056,
-      "loss": 7.4892,
-      "step": 110
-    },
-    {
-      "epoch": 0.02,
-      "learning_rate": 0.000171993565594773,
-      "loss": 7.3256,
-      "step": 120
-    },
-    {
-      "epoch": 0.03,
-      "learning_rate": 0.00017507866443784335,
-      "loss": 7.1827,
-      "step": 130
-    },
-    {
-      "epoch": 0.03,
-      "learning_rate": 0.0001779224840062419,
-      "loss": 6.9698,
-      "step": 140
-    },
-    {
-      "epoch": 0.03,
-      "learning_rate": 0.00018056004207494319,
-      "loss": 6.8162,
-      "step": 150
-    },
-    {
-      "epoch": 0.03,
-      "eval_accuracy": 0.1567556253270539,
-      "eval_loss": 7.08203125,
-      "eval_runtime": 6.5908,
-      "eval_samples_per_second": 5.31,
-      "eval_steps_per_second": 0.303,
-      "step": 150
-    },
-    {
-      "epoch": 0.03,
-      "learning_rate": 0.00018301924610008189,
-      "loss": 6.6293,
-      "step": 160
-    },
-    {
-      "epoch": 0.03,
-      "learning_rate": 0.00018532269677939782,
-      "loss": 6.4114,
-      "step": 170
-    },
-    {
-      "epoch": 0.04,
-      "learning_rate": 0.00018748895370481112,
-      "loss": 6.2911,
-      "step": 180
-    },
-    {
-      "epoch": 0.04,
-      "learning_rate": 0.00018953344483335556,
-      "loss": 6.1047,
-      "step": 190
-    },
-    {
-      "epoch": 0.04,
-      "learning_rate": 0.00019146913367833817,
-      "loss": 5.9957,
-      "step": 200
-    },
-    {
-      "epoch": 0.04,
-      "eval_accuracy": 0.19811616954474098,
-      "eval_loss": 6.4296875,
-      "eval_runtime": 6.5897,
-      "eval_samples_per_second": 5.311,
-      "eval_steps_per_second": 0.304,
-      "step": 200
-    },
-    {
-      "epoch": 0.04,
-      "learning_rate": 0.00019330701776944063,
-      "loss": 5.8281,
-      "step": 210
-    },
-    {
-      "epoch": 0.05,
-      "learning_rate": 0.00019505650713185044,
-      "loss": 5.6927,
-      "step": 220
-    },
-    {
-      "epoch": 0.05,
-      "learning_rate": 0.00019672571585424665,
-      "loss": 5.5564,
-      "step": 230
-    },
-    {
-      "epoch": 0.05,
-      "learning_rate": 0.00019832168964685297,
-      "loss": 5.3813,
-      "step": 240
-    },
-    {
-      "epoch": 0.05,
-      "learning_rate": 0.0001998505855457085,
-      "loss": 5.2496,
-      "step": 250
-    },
-    {
-      "epoch": 0.05,
-      "eval_accuracy": 0.24055817198674342,
-      "eval_loss": 5.8203125,
-      "eval_runtime": 6.6013,
-      "eval_samples_per_second": 5.302,
-      "eval_steps_per_second": 0.303,
-      "step": 250
-    },
-    {
-      "epoch": 0.05,
-      "learning_rate": 0.0001996535296665223,
-      "loss": 5.13,
-      "step": 260
-    },
-    {
-      "epoch": 0.06,
-      "learning_rate": 0.0001992204417496752,
-      "loss": 5.0354,
-      "step": 270
-    },
-    {
-      "epoch": 0.06,
-      "learning_rate": 0.00019878735383282807,
-      "loss": 4.9021,
-      "step": 280
-    },
-    {
-      "epoch": 0.06,
-      "learning_rate": 0.00019835426591598097,
-      "loss": 4.8181,
-      "step": 290
-    },
-    {
-      "epoch": 0.06,
-      "learning_rate": 0.00019792117799913384,
-      "loss": 4.6993,
-      "step": 300
-    },
-    {
-      "epoch": 0.06,
-      "eval_accuracy": 0.2641758241758242,
-      "eval_loss": 5.41796875,
-      "eval_runtime": 6.6009,
-      "eval_samples_per_second": 5.302,
-      "eval_steps_per_second": 0.303,
-      "step": 300
-    },
-    {
-      "epoch": 0.06,
-      "learning_rate": 0.0001974880900822867,
-      "loss": 4.6761,
-      "step": 310
-    },
-    {
-      "epoch": 0.07,
-      "learning_rate": 0.00019705500216543958,
-      "loss": 4.5908,
-      "step": 320
-    },
-    {
-      "epoch": 0.07,
-      "learning_rate": 0.00019662191424859245,
-      "loss": 4.5301,
-      "step": 330
-    },
-    {
-      "epoch": 0.07,
-      "learning_rate": 0.00019618882633174535,
-      "loss": 4.4729,
-      "step": 340
-    },
-    {
-      "epoch": 0.07,
-      "learning_rate": 0.00019575573841489822,
-      "loss": 4.3928,
-      "step": 350
-    },
-    {
-      "epoch": 0.07,
-      "eval_accuracy": 0.27927437641723357,
-      "eval_loss": 5.14453125,
-      "eval_runtime": 6.6101,
-      "eval_samples_per_second": 5.295,
-      "eval_steps_per_second": 0.303,
-      "step": 350
-    },
-    {
-      "epoch": 0.07,
-      "learning_rate": 0.00019532265049805112,
-      "loss": 4.4083,
-      "step": 360
-    },
-    {
-      "epoch": 0.08,
-      "learning_rate": 0.000194889562581204,
-      "loss": 4.336,
-      "step": 370
-    },
-    {
-      "epoch": 0.08,
-      "learning_rate": 0.0001944564746643569,
-      "loss": 4.2714,
-      "step": 380
-    },
-    {
-      "epoch": 0.08,
-      "learning_rate": 0.00019402338674750976,
-      "loss": 4.2124,
-      "step": 390
-    },
-    {
-      "epoch": 0.08,
-      "learning_rate": 0.00019359029883066263,
-      "loss": 4.2395,
-      "step": 400
-    },
-    {
-      "epoch": 0.08,
-      "eval_accuracy": 0.2894470608756323,
-      "eval_loss": 4.96875,
-      "eval_runtime": 6.5945,
-      "eval_samples_per_second": 5.307,
-      "eval_steps_per_second": 0.303,
-      "step": 400
-    },
-    {
-      "epoch": 0.08,
-      "learning_rate": 0.0001931572109138155,
-      "loss": 4.1867,
-      "step": 410
-    },
-    {
-      "epoch": 0.09,
-      "learning_rate": 0.0001927241229969684,
-      "loss": 4.1687,
-      "step": 420
-    },
-    {
-      "epoch": 0.09,
-      "learning_rate": 0.00019229103508012127,
-      "loss": 4.1027,
-      "step": 430
-    },
-    {
-      "epoch": 0.09,
-      "learning_rate": 0.00019185794716327414,
-      "loss": 4.1233,
-      "step": 440
-    },
-    {
-      "epoch": 0.09,
-      "learning_rate": 0.00019142485924642704,
-      "loss": 4.0781,
-      "step": 450
-    },
-    {
-      "epoch": 0.09,
-      "eval_accuracy": 0.29638932496075354,
-      "eval_loss": 4.8515625,
-      "eval_runtime": 6.5996,
-      "eval_samples_per_second": 5.303,
-      "eval_steps_per_second": 0.303,
-      "step": 450
-    },
-    {
-      "epoch": 0.09,
-      "learning_rate": 0.0001909917713295799,
-      "loss": 4.0855,
-      "step": 460
-    },
-    {
-      "epoch": 0.1,
-      "learning_rate": 0.0001905586834127328,
-      "loss": 4.0859,
-      "step": 470
-    },
-    {
-      "epoch": 0.1,
-      "learning_rate": 0.00019012559549588568,
-      "loss": 4.0124,
-      "step": 480
-    },
-    {
-      "epoch": 0.1,
-      "learning_rate": 0.00018969250757903855,
-      "loss": 4.0151,
-      "step": 490
-    },
-    {
-      "epoch": 0.1,
-      "learning_rate": 0.00018925941966219142,
-      "loss": 4.0409,
-      "step": 500
-    },
-    {
-      "epoch": 0.1,
-      "eval_accuracy": 0.30181057038199893,
-      "eval_loss": 4.76953125,
-      "eval_runtime": 6.6117,
-      "eval_samples_per_second": 5.294,
-      "eval_steps_per_second": 0.302,
-      "step": 500
-    },
-    {
-      "epoch": 0.1,
-      "learning_rate": 0.00018882633174534431,
-      "loss": 3.9912,
-      "step": 510
-    },
-    {
-      "epoch": 0.11,
-      "learning_rate": 0.00018839324382849718,
-      "loss": 3.9383,
-      "step": 520
-    },
-    {
-      "epoch": 0.11,
-      "learning_rate": 0.00018796015591165008,
-      "loss": 3.9764,
-      "step": 530
-    },
-    {
-      "epoch": 0.11,
-      "learning_rate": 0.00018752706799480295,
-      "loss": 3.9809,
-      "step": 540
-    },
-    {
-      "epoch": 0.11,
-      "learning_rate": 0.00018709398007795585,
-      "loss": 3.9178,
-      "step": 550
-    },
-    {
-      "epoch": 0.11,
-      "eval_accuracy": 0.30575963718820864,
-      "eval_loss": 4.703125,
-      "eval_runtime": 6.6265,
-      "eval_samples_per_second": 5.282,
-      "eval_steps_per_second": 0.302,
-      "step": 550
-    },
-    {
-      "epoch": 0.12,
-      "learning_rate": 0.00018666089216110872,
-      "loss": 3.9073,
-      "step": 560
-    },
-    {
-      "epoch": 0.12,
-      "learning_rate": 0.0001862278042442616,
-      "loss": 3.9459,
-      "step": 570
-    },
-    {
-      "epoch": 0.12,
-      "learning_rate": 0.00018579471632741446,
-      "loss": 3.9535,
-      "step": 580
-    },
-    {
-      "epoch": 0.12,
-      "learning_rate": 0.00018536162841056733,
-      "loss": 3.8982,
-      "step": 590
-    },
-    {
-      "epoch": 0.12,
-      "learning_rate": 0.00018492854049372023,
-      "loss": 3.834,
-      "step": 600
-    },
-    {
-      "epoch": 0.12,
-      "eval_accuracy": 0.3082574568288854,
-      "eval_loss": 4.65625,
-      "eval_runtime": 6.6135,
-      "eval_samples_per_second": 5.292,
-      "eval_steps_per_second": 0.302,
-      "step": 600
-    },
-    {
-      "epoch": 0.13,
-      "learning_rate": 0.0001844954525768731,
-      "loss": 3.8759,
-      "step": 610
-    },
-    {
-      "epoch": 0.13,
-      "learning_rate": 0.000184062364660026,
-      "loss": 3.8835,
-      "step": 620
-    },
-    {
-      "epoch": 0.13,
-      "learning_rate": 0.00018362927674317887,
-      "loss": 3.9003,
-      "step": 630
-    },
-    {
-      "epoch": 0.13,
-      "learning_rate": 0.00018319618882633177,
-      "loss": 3.8538,
-      "step": 640
-    },
-    {
-      "epoch": 0.13,
-      "learning_rate": 0.00018276310090948464,
-      "loss": 3.8316,
-      "step": 650
-    },
-    {
-      "epoch": 0.13,
-      "eval_accuracy": 0.30981336124193265,
-      "eval_loss": 4.625,
-      "eval_runtime": 6.6039,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 650
-    },
-    {
-      "epoch": 0.14,
-      "learning_rate": 0.00018233001299263754,
-      "loss": 3.9085,
-      "step": 660
-    },
-    {
-      "epoch": 0.14,
-      "learning_rate": 0.00018189692507579038,
-      "loss": 3.7825,
-      "step": 670
-    },
-    {
-      "epoch": 0.14,
-      "learning_rate": 0.00018146383715894328,
-      "loss": 3.824,
-      "step": 680
-    },
-    {
-      "epoch": 0.14,
-      "learning_rate": 0.00018103074924209615,
-      "loss": 3.8457,
-      "step": 690
-    },
-    {
-      "epoch": 0.14,
-      "learning_rate": 0.00018059766132524902,
-      "loss": 3.8197,
-      "step": 700
-    },
-    {
-      "epoch": 0.14,
-      "eval_accuracy": 0.3116902145473574,
-      "eval_loss": 4.59765625,
-      "eval_runtime": 6.6155,
-      "eval_samples_per_second": 5.291,
-      "eval_steps_per_second": 0.302,
-      "step": 700
-    },
-    {
-      "epoch": 0.15,
-      "learning_rate": 0.00018016457340840192,
-      "loss": 3.79,
-      "step": 710
-    },
-    {
-      "epoch": 0.15,
-      "learning_rate": 0.00017973148549155479,
-      "loss": 3.7907,
-      "step": 720
-    },
-    {
-      "epoch": 0.15,
-      "learning_rate": 0.00017929839757470768,
-      "loss": 3.7797,
-      "step": 730
-    },
-    {
-      "epoch": 0.15,
-      "learning_rate": 0.00017886530965786055,
-      "loss": 3.7533,
-      "step": 740
-    },
-    {
-      "epoch": 0.15,
-      "learning_rate": 0.00017843222174101345,
-      "loss": 3.7464,
-      "step": 750
-    },
-    {
-      "epoch": 0.15,
-      "eval_accuracy": 0.31469038897610324,
-      "eval_loss": 4.5625,
-      "eval_runtime": 6.5988,
-      "eval_samples_per_second": 5.304,
-      "eval_steps_per_second": 0.303,
-      "step": 750
-    },
-    {
-      "epoch": 0.16,
-      "learning_rate": 0.00017799913382416632,
-      "loss": 3.7347,
-      "step": 760
-    },
-    {
-      "epoch": 0.16,
-      "learning_rate": 0.0001775660459073192,
-      "loss": 3.7917,
-      "step": 770
-    },
-    {
-      "epoch": 0.16,
-      "learning_rate": 0.00017713295799047206,
-      "loss": 3.8106,
-      "step": 780
-    },
-    {
-      "epoch": 0.16,
-      "learning_rate": 0.00017669987007362496,
-      "loss": 3.7289,
-      "step": 790
-    },
-    {
-      "epoch": 0.16,
-      "learning_rate": 0.00017626678215677783,
-      "loss": 3.767,
-      "step": 800
-    },
-    {
-      "epoch": 0.16,
-      "eval_accuracy": 0.3163718820861678,
-      "eval_loss": 4.5390625,
-      "eval_runtime": 6.604,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 800
-    },
-    {
-      "epoch": 0.17,
-      "learning_rate": 0.00017583369423993073,
-      "loss": 3.7362,
-      "step": 810
-    },
-    {
-      "epoch": 0.17,
-      "learning_rate": 0.0001754006063230836,
-      "loss": 3.7474,
-      "step": 820
-    },
-    {
-      "epoch": 0.17,
-      "learning_rate": 0.00017496751840623647,
-      "loss": 3.7485,
-      "step": 830
-    },
-    {
-      "epoch": 0.17,
-      "learning_rate": 0.00017453443048938937,
-      "loss": 3.7341,
-      "step": 840
-    },
-    {
-      "epoch": 0.17,
-      "learning_rate": 0.00017410134257254224,
-      "loss": 3.7511,
-      "step": 850
-    },
-    {
-      "epoch": 0.17,
-      "eval_accuracy": 0.3172649572649573,
-      "eval_loss": 4.515625,
-      "eval_runtime": 6.6093,
-      "eval_samples_per_second": 5.296,
-      "eval_steps_per_second": 0.303,
-      "step": 850
-    },
-    {
-      "epoch": 0.18,
-      "learning_rate": 0.0001736682546556951,
-      "loss": 3.6862,
-      "step": 860
-    },
-    {
-      "epoch": 0.18,
-      "learning_rate": 0.00017323516673884798,
-      "loss": 3.6411,
-      "step": 870
-    },
-    {
-      "epoch": 0.18,
-      "learning_rate": 0.00017280207882200088,
-      "loss": 3.7181,
-      "step": 880
-    },
-    {
-      "epoch": 0.18,
-      "learning_rate": 0.00017236899090515375,
-      "loss": 3.6471,
-      "step": 890
-    },
-    {
-      "epoch": 0.19,
-      "learning_rate": 0.00017193590298830665,
-      "loss": 3.7166,
-      "step": 900
-    },
-    {
-      "epoch": 0.19,
-      "eval_accuracy": 0.3188278388278388,
-      "eval_loss": 4.4921875,
-      "eval_runtime": 6.6113,
-      "eval_samples_per_second": 5.294,
-      "eval_steps_per_second": 0.303,
-      "step": 900
-    },
-    {
-      "epoch": 0.19,
-      "learning_rate": 0.00017150281507145952,
-      "loss": 3.6869,
-      "step": 910
-    },
-    {
-      "epoch": 0.19,
-      "learning_rate": 0.00017106972715461241,
-      "loss": 3.6728,
-      "step": 920
-    },
-    {
-      "epoch": 0.19,
-      "learning_rate": 0.00017063663923776528,
-      "loss": 3.7705,
-      "step": 930
-    },
-    {
-      "epoch": 0.19,
-      "learning_rate": 0.00017020355132091815,
-      "loss": 3.6728,
-      "step": 940
-    },
-    {
-      "epoch": 0.2,
-      "learning_rate": 0.00016977046340407103,
-      "loss": 3.6908,
-      "step": 950
-    },
-    {
-      "epoch": 0.2,
-      "eval_accuracy": 0.3190441304727019,
-      "eval_loss": 4.48046875,
-      "eval_runtime": 6.5961,
-      "eval_samples_per_second": 5.306,
-      "eval_steps_per_second": 0.303,
-      "step": 950
-    },
-    {
-      "epoch": 0.2,
-      "learning_rate": 0.0001693373754872239,
-      "loss": 3.5988,
-      "step": 960
-    },
-    {
-      "epoch": 0.2,
-      "learning_rate": 0.0001689042875703768,
-      "loss": 3.6713,
-      "step": 970
-    },
-    {
-      "epoch": 0.2,
-      "learning_rate": 0.00016847119965352966,
-      "loss": 3.7165,
-      "step": 980
-    },
-    {
-      "epoch": 0.2,
-      "learning_rate": 0.00016803811173668256,
-      "loss": 3.7098,
-      "step": 990
-    },
-    {
-      "epoch": 0.21,
-      "learning_rate": 0.00016760502381983543,
-      "loss": 3.617,
-      "step": 1000
-    },
-    {
-      "epoch": 0.21,
-      "eval_accuracy": 0.3208442351299494,
-      "eval_loss": 4.46484375,
-      "eval_runtime": 6.5949,
-      "eval_samples_per_second": 5.307,
-      "eval_steps_per_second": 0.303,
-      "step": 1000
-    },
-    {
-      "epoch": 0.21,
-      "learning_rate": 0.00016717193590298833,
-      "loss": 3.6629,
-      "step": 1010
-    },
-    {
-      "epoch": 0.21,
-      "learning_rate": 0.0001667388479861412,
-      "loss": 3.684,
-      "step": 1020
-    },
-    {
-      "epoch": 0.21,
-      "learning_rate": 0.00016630576006929407,
-      "loss": 3.6877,
-      "step": 1030
-    },
-    {
-      "epoch": 0.21,
-      "learning_rate": 0.00016587267215244694,
-      "loss": 3.6274,
-      "step": 1040
-    },
-    {
-      "epoch": 0.22,
-      "learning_rate": 0.00016543958423559984,
-      "loss": 3.6734,
-      "step": 1050
-    },
-    {
-      "epoch": 0.22,
-      "eval_accuracy": 0.3213535670678528,
-      "eval_loss": 4.453125,
-      "eval_runtime": 6.5974,
-      "eval_samples_per_second": 5.305,
-      "eval_steps_per_second": 0.303,
-      "step": 1050
-    },
-    {
-      "epoch": 0.22,
-      "learning_rate": 0.0001650064963187527,
-      "loss": 3.6895,
-      "step": 1060
-    },
-    {
-      "epoch": 0.22,
-      "learning_rate": 0.0001645734084019056,
-      "loss": 3.6662,
-      "step": 1070
-    },
-    {
-      "epoch": 0.22,
-      "learning_rate": 0.00016414032048505848,
-      "loss": 3.6152,
-      "step": 1080
-    },
-    {
-      "epoch": 0.22,
-      "learning_rate": 0.00016370723256821135,
-      "loss": 3.6531,
-      "step": 1090
-    },
-    {
-      "epoch": 0.23,
-      "learning_rate": 0.00016327414465136425,
-      "loss": 3.6916,
-      "step": 1100
-    },
-    {
-      "epoch": 0.23,
-      "eval_accuracy": 0.32398395255538115,
-      "eval_loss": 4.43359375,
-      "eval_runtime": 6.5897,
-      "eval_samples_per_second": 5.311,
-      "eval_steps_per_second": 0.304,
-      "step": 1100
-    },
-    {
-      "epoch": 0.23,
-      "learning_rate": 0.00016284105673451712,
-      "loss": 3.6961,
-      "step": 1110
-    },
-    {
-      "epoch": 0.23,
-      "learning_rate": 0.00016240796881767,
-      "loss": 3.5811,
-      "step": 1120
-    },
-    {
-      "epoch": 0.23,
-      "learning_rate": 0.00016197488090082286,
-      "loss": 3.6161,
-      "step": 1130
-    },
-    {
-      "epoch": 0.23,
-      "learning_rate": 0.00016154179298397576,
-      "loss": 3.6305,
-      "step": 1140
-    },
-    {
-      "epoch": 0.24,
-      "learning_rate": 0.00016110870506712863,
-      "loss": 3.629,
-      "step": 1150
-    },
-    {
-      "epoch": 0.24,
-      "eval_accuracy": 0.32400488400488403,
-      "eval_loss": 4.421875,
-      "eval_runtime": 6.5985,
-      "eval_samples_per_second": 5.304,
-      "eval_steps_per_second": 0.303,
-      "step": 1150
-    },
-    {
-      "epoch": 0.24,
-      "learning_rate": 0.00016067561715028152,
-      "loss": 3.6467,
-      "step": 1160
-    },
-    {
-      "epoch": 0.24,
-      "learning_rate": 0.0001602425292334344,
-      "loss": 3.6573,
-      "step": 1170
-    },
-    {
-      "epoch": 0.24,
-      "learning_rate": 0.0001598094413165873,
-      "loss": 3.6372,
-      "step": 1180
-    },
-    {
-      "epoch": 0.24,
-      "learning_rate": 0.00015937635339974016,
-      "loss": 3.6369,
-      "step": 1190
-    },
-    {
-      "epoch": 0.25,
-      "learning_rate": 0.00015894326548289303,
-      "loss": 3.6001,
-      "step": 1200
-    },
-    {
-      "epoch": 0.25,
-      "eval_accuracy": 0.3247165532879819,
-      "eval_loss": 4.4140625,
-      "eval_runtime": 6.602,
-      "eval_samples_per_second": 5.301,
-      "eval_steps_per_second": 0.303,
-      "step": 1200
-    },
-    {
-      "epoch": 0.25,
-      "learning_rate": 0.0001585101775660459,
-      "loss": 3.5843,
-      "step": 1210
-    },
-    {
-      "epoch": 0.25,
-      "learning_rate": 0.00015807708964919877,
-      "loss": 3.6407,
-      "step": 1220
-    },
-    {
-      "epoch": 0.25,
-      "learning_rate": 0.00015764400173235167,
-      "loss": 3.6413,
-      "step": 1230
-    },
-    {
-      "epoch": 0.26,
-      "learning_rate": 0.00015721091381550454,
-      "loss": 3.5963,
-      "step": 1240
-    },
-    {
-      "epoch": 0.26,
-      "learning_rate": 0.00015677782589865744,
-      "loss": 3.6053,
-      "step": 1250
-    },
-    {
-      "epoch": 0.26,
-      "eval_accuracy": 0.32607709750566893,
-      "eval_loss": 4.40234375,
-      "eval_runtime": 6.5982,
-      "eval_samples_per_second": 5.304,
-      "eval_steps_per_second": 0.303,
-      "step": 1250
-    },
-    {
-      "epoch": 0.26,
-      "learning_rate": 0.0001563447379818103,
-      "loss": 3.6049,
-      "step": 1260
-    },
-    {
-      "epoch": 0.26,
-      "learning_rate": 0.0001559116500649632,
-      "loss": 3.6112,
-      "step": 1270
-    },
-    {
-      "epoch": 0.26,
-      "learning_rate": 0.00015547856214811608,
-      "loss": 3.5872,
-      "step": 1280
-    },
-    {
-      "epoch": 0.27,
-      "learning_rate": 0.00015504547423126895,
-      "loss": 3.6328,
-      "step": 1290
-    },
-    {
-      "epoch": 0.27,
-      "learning_rate": 0.00015461238631442182,
-      "loss": 3.5803,
-      "step": 1300
-    },
-    {
-      "epoch": 0.27,
-      "eval_accuracy": 0.32540729112157685,
-      "eval_loss": 4.390625,
-      "eval_runtime": 6.5995,
-      "eval_samples_per_second": 5.303,
-      "eval_steps_per_second": 0.303,
-      "step": 1300
-    },
-    {
-      "epoch": 0.27,
-      "learning_rate": 0.00015417929839757472,
-      "loss": 3.6153,
-      "step": 1310
-    },
-    {
-      "epoch": 0.27,
-      "learning_rate": 0.0001537462104807276,
-      "loss": 3.5493,
-      "step": 1320
-    },
-    {
-      "epoch": 0.27,
-      "learning_rate": 0.00015331312256388049,
-      "loss": 3.5854,
-      "step": 1330
-    },
-    {
-      "epoch": 0.28,
-      "learning_rate": 0.00015288003464703336,
-      "loss": 3.6029,
-      "step": 1340
-    },
-    {
-      "epoch": 0.28,
-      "learning_rate": 0.00015244694673018623,
-      "loss": 3.5886,
-      "step": 1350
-    },
-    {
-      "epoch": 0.28,
-      "eval_accuracy": 0.32738880167451595,
-      "eval_loss": 4.37890625,
-      "eval_runtime": 6.5931,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 1350
-    },
-    {
-      "epoch": 0.28,
-      "learning_rate": 0.00015201385881333913,
-      "loss": 3.5496,
-      "step": 1360
-    },
-    {
-      "epoch": 0.28,
-      "learning_rate": 0.000151580770896492,
-      "loss": 3.557,
-      "step": 1370
-    },
-    {
-      "epoch": 0.28,
-      "learning_rate": 0.00015114768297964487,
-      "loss": 3.5647,
-      "step": 1380
-    },
-    {
-      "epoch": 0.29,
-      "learning_rate": 0.00015071459506279774,
-      "loss": 3.5912,
-      "step": 1390
-    },
-    {
-      "epoch": 0.29,
-      "learning_rate": 0.00015028150714595063,
-      "loss": 3.5033,
-      "step": 1400
-    },
-    {
-      "epoch": 0.29,
-      "eval_accuracy": 0.3287981859410431,
-      "eval_loss": 4.3671875,
-      "eval_runtime": 6.593,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 1400
-    },
-    {
-      "epoch": 0.29,
-      "learning_rate": 0.0001498484192291035,
-      "loss": 3.5814,
-      "step": 1410
-    },
-    {
-      "epoch": 0.29,
-      "learning_rate": 0.0001494153313122564,
-      "loss": 3.5834,
-      "step": 1420
-    },
-    {
-      "epoch": 0.29,
-      "learning_rate": 0.00014898224339540927,
-      "loss": 3.5661,
-      "step": 1430
-    },
-    {
-      "epoch": 0.3,
-      "learning_rate": 0.00014854915547856217,
-      "loss": 3.5844,
-      "step": 1440
-    },
-    {
-      "epoch": 0.3,
-      "learning_rate": 0.00014811606756171504,
-      "loss": 3.58,
-      "step": 1450
-    },
-    {
-      "epoch": 0.3,
-      "eval_accuracy": 0.3283656026513169,
-      "eval_loss": 4.36328125,
-      "eval_runtime": 6.6004,
-      "eval_samples_per_second": 5.303,
-      "eval_steps_per_second": 0.303,
-      "step": 1450
-    },
-    {
-      "epoch": 0.3,
-      "learning_rate": 0.0001476829796448679,
-      "loss": 3.5874,
-      "step": 1460
-    },
-    {
-      "epoch": 0.3,
-      "learning_rate": 0.00014724989172802078,
-      "loss": 3.5736,
-      "step": 1470
-    },
-    {
-      "epoch": 0.3,
-      "learning_rate": 0.00014681680381117365,
-      "loss": 3.5659,
-      "step": 1480
-    },
-    {
-      "epoch": 0.31,
-      "learning_rate": 0.00014638371589432655,
-      "loss": 3.5632,
-      "step": 1490
-    },
-    {
-      "epoch": 0.31,
-      "learning_rate": 0.00014595062797747942,
-      "loss": 3.4966,
-      "step": 1500
-    },
-    {
-      "epoch": 0.31,
-      "eval_accuracy": 0.32832373975231116,
-      "eval_loss": 4.3515625,
-      "eval_runtime": 6.592,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 1500
-    },
-    {
-      "epoch": 0.31,
-      "learning_rate": 0.00014551754006063232,
-      "loss": 3.5236,
-      "step": 1510
-    },
-    {
-      "epoch": 0.31,
-      "learning_rate": 0.0001450844521437852,
-      "loss": 3.5277,
-      "step": 1520
-    },
-    {
-      "epoch": 0.31,
-      "learning_rate": 0.0001446513642269381,
-      "loss": 3.5237,
-      "step": 1530
-    },
-    {
-      "epoch": 0.32,
-      "learning_rate": 0.00014421827631009096,
-      "loss": 3.5719,
-      "step": 1540
-    },
-    {
-      "epoch": 0.32,
-      "learning_rate": 0.00014378518839324383,
-      "loss": 3.5411,
-      "step": 1550
-    },
-    {
-      "epoch": 0.32,
-      "eval_accuracy": 0.32884004884004886,
-      "eval_loss": 4.3515625,
-      "eval_runtime": 6.6086,
-      "eval_samples_per_second": 5.296,
-      "eval_steps_per_second": 0.303,
-      "step": 1550
-    },
-    {
-      "epoch": 0.32,
-      "learning_rate": 0.0001433521004763967,
-      "loss": 3.5287,
-      "step": 1560
-    },
-    {
-      "epoch": 0.32,
-      "learning_rate": 0.0001429190125595496,
-      "loss": 3.5965,
-      "step": 1570
-    },
-    {
-      "epoch": 0.32,
-      "learning_rate": 0.00014248592464270247,
-      "loss": 3.5435,
-      "step": 1580
-    },
-    {
-      "epoch": 0.33,
-      "learning_rate": 0.00014205283672585536,
-      "loss": 3.5536,
-      "step": 1590
-    },
-    {
-      "epoch": 0.33,
-      "learning_rate": 0.00014161974880900824,
-      "loss": 3.527,
-      "step": 1600
-    },
-    {
-      "epoch": 0.33,
-      "eval_accuracy": 0.33027036455607883,
-      "eval_loss": 4.33984375,
-      "eval_runtime": 6.5917,
-      "eval_samples_per_second": 5.31,
-      "eval_steps_per_second": 0.303,
-      "step": 1600
-    },
-    {
-      "epoch": 0.33,
-      "learning_rate": 0.0001411866608921611,
-      "loss": 3.5765,
-      "step": 1610
-    },
-    {
-      "epoch": 0.33,
-      "learning_rate": 0.000140753572975314,
-      "loss": 3.5882,
-      "step": 1620
-    },
-    {
-      "epoch": 0.34,
-      "learning_rate": 0.00014032048505846687,
-      "loss": 3.5135,
-      "step": 1630
-    },
-    {
-      "epoch": 0.34,
-      "learning_rate": 0.00013988739714161974,
-      "loss": 3.4924,
-      "step": 1640
-    },
-    {
-      "epoch": 0.34,
-      "learning_rate": 0.00013945430922477262,
-      "loss": 3.6018,
-      "step": 1650
-    },
-    {
-      "epoch": 0.34,
-      "eval_accuracy": 0.3299284842141985,
-      "eval_loss": 4.33203125,
-      "eval_runtime": 6.5992,
-      "eval_samples_per_second": 5.304,
-      "eval_steps_per_second": 0.303,
-      "step": 1650
-    },
-    {
-      "epoch": 0.34,
-      "learning_rate": 0.0001390212213079255,
-      "loss": 3.5646,
-      "step": 1660
-    },
-    {
-      "epoch": 0.34,
-      "learning_rate": 0.00013858813339107838,
-      "loss": 3.5164,
-      "step": 1670
-    },
-    {
-      "epoch": 0.35,
-      "learning_rate": 0.00013815504547423128,
-      "loss": 3.5433,
-      "step": 1680
-    },
-    {
-      "epoch": 0.35,
-      "learning_rate": 0.00013772195755738415,
-      "loss": 3.4929,
-      "step": 1690
-    },
-    {
-      "epoch": 0.35,
-      "learning_rate": 0.00013728886964053705,
-      "loss": 3.4802,
-      "step": 1700
-    },
-    {
-      "epoch": 0.35,
-      "eval_accuracy": 0.3301238444095587,
-      "eval_loss": 4.32421875,
-      "eval_runtime": 6.5899,
-      "eval_samples_per_second": 5.311,
-      "eval_steps_per_second": 0.303,
-      "step": 1700
-    },
-    {
-      "epoch": 0.35,
-      "learning_rate": 0.00013685578172368992,
-      "loss": 3.5211,
-      "step": 1710
-    },
-    {
-      "epoch": 0.35,
-      "learning_rate": 0.00013642269380684282,
-      "loss": 3.5424,
-      "step": 1720
-    },
-    {
-      "epoch": 0.36,
-      "learning_rate": 0.00013598960588999566,
-      "loss": 3.468,
-      "step": 1730
-    },
-    {
-      "epoch": 0.36,
-      "learning_rate": 0.00013555651797314853,
-      "loss": 3.5342,
-      "step": 1740
-    },
-    {
-      "epoch": 0.36,
-      "learning_rate": 0.00013512343005630143,
-      "loss": 3.4375,
-      "step": 1750
-    },
-    {
-      "epoch": 0.36,
-      "eval_accuracy": 0.330584336298622,
-      "eval_loss": 4.32421875,
-      "eval_runtime": 6.5928,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 1750
-    },
-    {
-      "epoch": 0.36,
-      "learning_rate": 0.0001346903421394543,
-      "loss": 3.5239,
-      "step": 1760
-    },
-    {
-      "epoch": 0.36,
-      "learning_rate": 0.0001342572542226072,
-      "loss": 3.5176,
-      "step": 1770
-    },
-    {
-      "epoch": 0.37,
-      "learning_rate": 0.00013382416630576007,
-      "loss": 3.4992,
-      "step": 1780
-    },
-    {
-      "epoch": 0.37,
-      "learning_rate": 0.00013339107838891297,
-      "loss": 3.457,
-      "step": 1790
-    },
-    {
-      "epoch": 0.37,
-      "learning_rate": 0.00013295799047206584,
-      "loss": 3.4873,
-      "step": 1800
-    },
-    {
-      "epoch": 0.37,
-      "eval_accuracy": 0.33109366823652536,
-      "eval_loss": 4.3203125,
-      "eval_runtime": 6.6125,
-      "eval_samples_per_second": 5.293,
-      "eval_steps_per_second": 0.302,
-      "step": 1800
-    },
-    {
-      "epoch": 0.37,
-      "learning_rate": 0.00013252490255521873,
-      "loss": 3.5021,
-      "step": 1810
-    },
-    {
-      "epoch": 0.37,
-      "learning_rate": 0.0001320918146383716,
-      "loss": 3.4871,
-      "step": 1820
-    },
-    {
-      "epoch": 0.38,
-      "learning_rate": 0.00013165872672152448,
-      "loss": 3.47,
-      "step": 1830
-    },
-    {
-      "epoch": 0.38,
-      "learning_rate": 0.00013122563880467735,
-      "loss": 3.4462,
-      "step": 1840
-    },
-    {
-      "epoch": 0.38,
-      "learning_rate": 0.00013079255088783024,
-      "loss": 3.435,
-      "step": 1850
-    },
-    {
-      "epoch": 0.38,
-      "eval_accuracy": 0.3309401709401709,
-      "eval_loss": 4.3125,
-      "eval_runtime": 6.5916,
-      "eval_samples_per_second": 5.31,
-      "eval_steps_per_second": 0.303,
-      "step": 1850
-    },
-    {
-      "epoch": 0.38,
-      "learning_rate": 0.00013035946297098311,
-      "loss": 3.4994,
-      "step": 1860
-    },
-    {
-      "epoch": 0.38,
-      "learning_rate": 0.00012992637505413598,
-      "loss": 3.5665,
-      "step": 1870
-    },
-    {
-      "epoch": 0.39,
-      "learning_rate": 0.00012949328713728888,
-      "loss": 3.5552,
-      "step": 1880
-    },
-    {
-      "epoch": 0.39,
-      "learning_rate": 0.00012906019922044175,
-      "loss": 3.5092,
-      "step": 1890
-    },
-    {
-      "epoch": 0.39,
-      "learning_rate": 0.00012862711130359465,
-      "loss": 3.4335,
-      "step": 1900
-    },
-    {
-      "epoch": 0.39,
-      "eval_accuracy": 0.3317634746206175,
-      "eval_loss": 4.3046875,
-      "eval_runtime": 6.6183,
-      "eval_samples_per_second": 5.288,
-      "eval_steps_per_second": 0.302,
-      "step": 1900
-    },
-    {
-      "epoch": 0.39,
-      "learning_rate": 0.00012819402338674752,
-      "loss": 3.5154,
-      "step": 1910
-    },
-    {
-      "epoch": 0.39,
-      "learning_rate": 0.0001277609354699004,
-      "loss": 3.5207,
-      "step": 1920
-    },
-    {
-      "epoch": 0.4,
-      "learning_rate": 0.00012732784755305326,
-      "loss": 3.4869,
-      "step": 1930
-    },
-    {
-      "epoch": 0.4,
-      "learning_rate": 0.00012689475963620616,
-      "loss": 3.4773,
-      "step": 1940
-    },
-    {
-      "epoch": 0.4,
-      "learning_rate": 0.00012646167171935903,
-      "loss": 3.4595,
-      "step": 1950
-    },
-    {
-      "epoch": 0.4,
-      "eval_accuracy": 0.3325239839525554,
-      "eval_loss": 4.296875,
-      "eval_runtime": 6.5968,
-      "eval_samples_per_second": 5.306,
-      "eval_steps_per_second": 0.303,
-      "step": 1950
-    },
-    {
-      "epoch": 0.4,
-      "learning_rate": 0.00012602858380251193,
-      "loss": 3.5035,
-      "step": 1960
-    },
-    {
-      "epoch": 0.41,
-      "learning_rate": 0.0001255954958856648,
-      "loss": 3.4692,
-      "step": 1970
-    },
-    {
-      "epoch": 0.41,
-      "learning_rate": 0.0001251624079688177,
-      "loss": 3.4712,
-      "step": 1980
-    },
-    {
-      "epoch": 0.41,
-      "learning_rate": 0.00012472932005197057,
-      "loss": 3.4558,
-      "step": 1990
-    },
-    {
-      "epoch": 0.41,
-      "learning_rate": 0.00012429623213512344,
-      "loss": 3.4937,
-      "step": 2000
-    },
-    {
-      "epoch": 0.41,
-      "eval_accuracy": 0.3319448805163091,
-      "eval_loss": 4.29296875,
-      "eval_runtime": 6.5908,
-      "eval_samples_per_second": 5.31,
-      "eval_steps_per_second": 0.303,
-      "step": 2000
-    },
-    {
-      "epoch": 0.41,
-      "learning_rate": 0.0001238631442182763,
-      "loss": 3.5093,
-      "step": 2010
-    },
-    {
-      "epoch": 0.42,
-      "learning_rate": 0.00012343005630142918,
-      "loss": 3.4941,
-      "step": 2020
-    },
-    {
-      "epoch": 0.42,
-      "learning_rate": 0.00012299696838458208,
-      "loss": 3.4706,
-      "step": 2030
-    },
-    {
-      "epoch": 0.42,
-      "learning_rate": 0.00012256388046773495,
-      "loss": 3.5106,
-      "step": 2040
-    },
-    {
-      "epoch": 0.42,
-      "learning_rate": 0.00012213079255088784,
-      "loss": 3.4959,
-      "step": 2050
-    },
-    {
-      "epoch": 0.42,
-      "eval_accuracy": 0.3324123495552067,
-      "eval_loss": 4.28515625,
-      "eval_runtime": 6.6003,
-      "eval_samples_per_second": 5.303,
-      "eval_steps_per_second": 0.303,
-      "step": 2050
-    },
-    {
-      "epoch": 0.42,
-      "learning_rate": 0.00012169770463404072,
-      "loss": 3.5218,
-      "step": 2060
-    },
-    {
-      "epoch": 0.43,
-      "learning_rate": 0.0001212646167171936,
-      "loss": 3.4813,
-      "step": 2070
-    },
-    {
-      "epoch": 0.43,
-      "learning_rate": 0.00012083152880034647,
-      "loss": 3.5107,
-      "step": 2080
-    },
-    {
-      "epoch": 0.43,
-      "learning_rate": 0.00012039844088349937,
-      "loss": 3.4568,
-      "step": 2090
-    },
-    {
-      "epoch": 0.43,
-      "learning_rate": 0.00011996535296665224,
-      "loss": 3.4987,
-      "step": 2100
-    },
-    {
-      "epoch": 0.43,
-      "eval_accuracy": 0.3331658817373103,
-      "eval_loss": 4.28515625,
-      "eval_runtime": 6.6107,
-      "eval_samples_per_second": 5.294,
-      "eval_steps_per_second": 0.303,
-      "step": 2100
-    },
-    {
-      "epoch": 0.43,
-      "learning_rate": 0.00011957557384148984,
-      "loss": 3.458,
-      "step": 2110
-    },
-    {
-      "epoch": 0.44,
-      "learning_rate": 0.0001191424859246427,
-      "loss": 3.4656,
-      "step": 2120
-    },
-    {
-      "epoch": 0.44,
-      "learning_rate": 0.00011870939800779559,
-      "loss": 3.4505,
-      "step": 2130
-    },
-    {
-      "epoch": 0.44,
-      "learning_rate": 0.00011827631009094846,
-      "loss": 3.4182,
-      "step": 2140
-    },
-    {
-      "epoch": 0.44,
-      "learning_rate": 0.00011784322217410136,
-      "loss": 3.4001,
-      "step": 2150
-    },
-    {
-      "epoch": 0.44,
-      "eval_accuracy": 0.3336403279260422,
-      "eval_loss": 4.28515625,
-      "eval_runtime": 6.5938,
-      "eval_samples_per_second": 5.308,
-      "eval_steps_per_second": 0.303,
-      "step": 2150
-    },
-    {
-      "epoch": 0.44,
-      "learning_rate": 0.00011741013425725423,
-      "loss": 3.4289,
-      "step": 2160
-    },
-    {
-      "epoch": 0.45,
-      "learning_rate": 0.00011697704634040711,
-      "loss": 3.4228,
-      "step": 2170
-    },
-    {
-      "epoch": 0.45,
-      "learning_rate": 0.00011654395842355998,
-      "loss": 3.4066,
-      "step": 2180
-    },
-    {
-      "epoch": 0.45,
-      "learning_rate": 0.00011611087050671288,
-      "loss": 3.4823,
-      "step": 2190
-    },
-    {
-      "epoch": 0.45,
-      "learning_rate": 0.00011567778258986575,
-      "loss": 3.4497,
-      "step": 2200
-    },
-    {
-      "epoch": 0.45,
-      "eval_accuracy": 0.3340380254665969,
-      "eval_loss": 4.28125,
-      "eval_runtime": 6.606,
-      "eval_samples_per_second": 5.298,
-      "eval_steps_per_second": 0.303,
-      "step": 2200
-    },
-    {
-      "epoch": 0.45,
-      "learning_rate": 0.00011524469467301864,
-      "loss": 3.4737,
-      "step": 2210
-    },
-    {
-      "epoch": 0.46,
-      "learning_rate": 0.0001148116067561715,
-      "loss": 3.4564,
-      "step": 2220
-    },
-    {
-      "epoch": 0.46,
-      "learning_rate": 0.00011437851883932438,
-      "loss": 3.4865,
-      "step": 2230
-    },
-    {
-      "epoch": 0.46,
-      "learning_rate": 0.00011394543092247727,
-      "loss": 3.4496,
-      "step": 2240
-    },
-    {
-      "epoch": 0.46,
-      "learning_rate": 0.00011351234300563015,
-      "loss": 3.4068,
-      "step": 2250
-    },
-    {
-      "epoch": 0.46,
-      "eval_accuracy": 0.33329147043432755,
-      "eval_loss": 4.27734375,
-      "eval_runtime": 6.6133,
-      "eval_samples_per_second": 5.292,
-      "eval_steps_per_second": 0.302,
-      "step": 2250
-    },
-    {
-      "epoch": 0.46,
-      "learning_rate": 0.00011307925508878303,
-      "loss": 3.4337,
-      "step": 2260
-    },
-    {
-      "epoch": 0.47,
-      "learning_rate": 0.0001126461671719359,
-      "loss": 3.4338,
-      "step": 2270
-    },
-    {
-      "epoch": 0.47,
-      "learning_rate": 0.0001122130792550888,
-      "loss": 3.4241,
-      "step": 2280
-    },
-    {
-      "epoch": 0.47,
-      "learning_rate": 0.00011177999133824167,
-      "loss": 3.497,
-      "step": 2290
-    },
-    {
-      "epoch": 0.47,
-      "learning_rate": 0.00011134690342139455,
-      "loss": 3.4634,
-      "step": 2300
-    },
-    {
-      "epoch": 0.47,
-      "eval_accuracy": 0.3339822082679226,
-      "eval_loss": 4.2734375,
-      "eval_runtime": 6.5925,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 2300
-    },
-    {
-      "epoch": 0.48,
-      "learning_rate": 0.00011091381550454742,
-      "loss": 3.4245,
-      "step": 2310
-    },
-    {
-      "epoch": 0.48,
-      "learning_rate": 0.00011048072758770032,
-      "loss": 3.4465,
-      "step": 2320
-    },
-    {
-      "epoch": 0.48,
-      "learning_rate": 0.00011004763967085319,
-      "loss": 3.498,
-      "step": 2330
-    },
-    {
-      "epoch": 0.48,
-      "learning_rate": 0.00010961455175400607,
-      "loss": 3.3637,
-      "step": 2340
-    },
-    {
-      "epoch": 0.48,
-      "learning_rate": 0.00010918146383715895,
-      "loss": 3.4324,
-      "step": 2350
-    },
-    {
-      "epoch": 0.48,
-      "eval_accuracy": 0.333849642421071,
-      "eval_loss": 4.27734375,
-      "eval_runtime": 6.5996,
-      "eval_samples_per_second": 5.303,
-      "eval_steps_per_second": 0.303,
-      "step": 2350
-    },
-    {
-      "epoch": 0.49,
-      "learning_rate": 0.00010874837592031182,
-      "loss": 3.3816,
-      "step": 2360
-    },
-    {
-      "epoch": 0.49,
-      "learning_rate": 0.00010831528800346471,
-      "loss": 3.4245,
-      "step": 2370
-    },
-    {
-      "epoch": 0.49,
-      "learning_rate": 0.00010788220008661758,
-      "loss": 3.4567,
-      "step": 2380
-    },
-    {
-      "epoch": 0.49,
-      "learning_rate": 0.00010744911216977047,
-      "loss": 3.4122,
-      "step": 2390
-    },
-    {
-      "epoch": 0.49,
-      "learning_rate": 0.00010701602425292334,
-      "loss": 3.4039,
-      "step": 2400
-    },
-    {
-      "epoch": 0.49,
-      "eval_accuracy": 0.3344147915576487,
-      "eval_loss": 4.265625,
-      "eval_runtime": 6.5992,
-      "eval_samples_per_second": 5.304,
-      "eval_steps_per_second": 0.303,
-      "step": 2400
-    },
-    {
-      "epoch": 0.5,
-      "learning_rate": 0.00010658293633607624,
-      "loss": 3.4124,
-      "step": 2410
-    },
-    {
-      "epoch": 0.5,
-      "learning_rate": 0.00010614984841922911,
-      "loss": 3.4309,
-      "step": 2420
-    },
-    {
-      "epoch": 0.5,
-      "learning_rate": 0.00010571676050238199,
-      "loss": 3.4464,
-      "step": 2430
-    },
-    {
-      "epoch": 0.5,
-      "learning_rate": 0.00010528367258553486,
-      "loss": 3.4136,
-      "step": 2440
-    },
-    {
-      "epoch": 0.5,
-      "learning_rate": 0.00010485058466868776,
-      "loss": 3.4502,
-      "step": 2450
-    },
-    {
-      "epoch": 0.5,
-      "eval_accuracy": 0.33453340310483165,
-      "eval_loss": 4.265625,
-      "eval_runtime": 6.7512,
-      "eval_samples_per_second": 5.184,
-      "eval_steps_per_second": 0.296,
-      "step": 2450
-    },
-    {
-      "epoch": 0.51,
-      "learning_rate": 0.00010441749675184063,
-      "loss": 3.4062,
-      "step": 2460
-    },
-    {
-      "epoch": 0.51,
-      "learning_rate": 0.00010398440883499351,
-      "loss": 3.3951,
-      "step": 2470
-    },
-    {
-      "epoch": 0.51,
-      "learning_rate": 0.00010355132091814638,
-      "loss": 3.4322,
-      "step": 2480
-    },
-    {
-      "epoch": 0.51,
-      "learning_rate": 0.00010311823300129926,
-      "loss": 3.4107,
-      "step": 2490
-    },
-    {
-      "epoch": 0.51,
-      "learning_rate": 0.00010268514508445215,
-      "loss": 3.4104,
-      "step": 2500
-    },
-    {
-      "epoch": 0.51,
-      "eval_accuracy": 0.33495900924472355,
-      "eval_loss": 4.2578125,
-      "eval_runtime": 6.5889,
-      "eval_samples_per_second": 5.312,
-      "eval_steps_per_second": 0.304,
-      "step": 2500
-    },
-    {
-      "epoch": 0.52,
-      "learning_rate": 0.00010225205716760502,
-      "loss": 3.4181,
-      "step": 2510
-    },
-    {
-      "epoch": 0.52,
-      "learning_rate": 0.00010181896925075791,
-      "loss": 3.3979,
-      "step": 2520
-    },
-    {
-      "epoch": 0.52,
-      "learning_rate": 0.00010138588133391078,
-      "loss": 3.3911,
-      "step": 2530
-    },
-    {
-      "epoch": 0.52,
-      "learning_rate": 0.00010095279341706368,
-      "loss": 3.4303,
-      "step": 2540
-    },
-    {
-      "epoch": 0.52,
-      "learning_rate": 0.00010051970550021655,
-      "loss": 3.5251,
-      "step": 2550
-    },
-    {
-      "epoch": 0.52,
-      "eval_accuracy": 0.33599860457003317,
-      "eval_loss": 4.2578125,
-      "eval_runtime": 6.5946,
-      "eval_samples_per_second": 5.307,
-      "eval_steps_per_second": 0.303,
-      "step": 2550
-    },
-    {
-      "epoch": 0.53,
-      "learning_rate": 0.00010008661758336943,
-      "loss": 3.4337,
-      "step": 2560
-    },
-    {
-      "epoch": 0.53,
-      "learning_rate": 9.96535296665223e-05,
-      "loss": 3.3906,
-      "step": 2570
-    },
-    {
-      "epoch": 0.53,
-      "learning_rate": 9.922044174967519e-05,
-      "loss": 3.4714,
-      "step": 2580
-    },
-    {
-      "epoch": 0.53,
-      "learning_rate": 9.878735383282807e-05,
-      "loss": 3.4061,
-      "step": 2590
-    },
-    {
-      "epoch": 0.53,
-      "learning_rate": 9.835426591598095e-05,
-      "loss": 3.4176,
-      "step": 2600
-    },
-    {
-      "epoch": 0.53,
-      "eval_accuracy": 0.3363404849119135,
-      "eval_loss": 4.25,
-      "eval_runtime": 6.594,
-      "eval_samples_per_second": 5.308,
-      "eval_steps_per_second": 0.303,
-      "step": 2600
-    },
-    {
-      "epoch": 0.54,
-      "learning_rate": 9.792117799913382e-05,
-      "loss": 3.37,
-      "step": 2610
-    },
-    {
-      "epoch": 0.54,
-      "learning_rate": 9.748809008228671e-05,
-      "loss": 3.3931,
-      "step": 2620
-    },
-    {
-      "epoch": 0.54,
-      "learning_rate": 9.705500216543959e-05,
-      "loss": 3.4052,
-      "step": 2630
-    },
-    {
-      "epoch": 0.54,
-      "learning_rate": 9.662191424859248e-05,
-      "loss": 3.4269,
-      "step": 2640
-    },
-    {
-      "epoch": 0.54,
-      "learning_rate": 9.618882633174535e-05,
-      "loss": 3.3795,
-      "step": 2650
-    },
-    {
-      "epoch": 0.54,
-      "eval_accuracy": 0.3354404325832897,
-      "eval_loss": 4.25,
-      "eval_runtime": 6.6039,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 2650
-    },
-    {
-      "epoch": 0.55,
-      "learning_rate": 9.575573841489823e-05,
-      "loss": 3.4064,
-      "step": 2660
-    },
-    {
-      "epoch": 0.55,
-      "learning_rate": 9.532265049805112e-05,
-      "loss": 3.4207,
-      "step": 2670
-    },
-    {
-      "epoch": 0.55,
-      "learning_rate": 9.488956258120399e-05,
-      "loss": 3.4353,
-      "step": 2680
-    },
-    {
-      "epoch": 0.55,
-      "learning_rate": 9.445647466435687e-05,
-      "loss": 3.4497,
-      "step": 2690
-    },
-    {
-      "epoch": 0.56,
-      "learning_rate": 9.402338674750974e-05,
-      "loss": 3.3656,
-      "step": 2700
-    },
-    {
-      "epoch": 0.56,
-      "eval_accuracy": 0.33636839351125064,
-      "eval_loss": 4.25,
-      "eval_runtime": 6.5948,
-      "eval_samples_per_second": 5.307,
-      "eval_steps_per_second": 0.303,
-      "step": 2700
-    },
-    {
-      "epoch": 0.56,
-      "learning_rate": 9.359029883066262e-05,
-      "loss": 3.3736,
-      "step": 2710
-    },
-    {
-      "epoch": 0.56,
-      "learning_rate": 9.315721091381551e-05,
-      "loss": 3.4236,
-      "step": 2720
-    },
-    {
-      "epoch": 0.56,
-      "learning_rate": 9.272412299696839e-05,
-      "loss": 3.4234,
-      "step": 2730
-    },
-    {
-      "epoch": 0.56,
-      "learning_rate": 9.229103508012126e-05,
-      "loss": 3.3849,
-      "step": 2740
-    },
-    {
-      "epoch": 0.57,
-      "learning_rate": 9.185794716327415e-05,
-      "loss": 3.3938,
-      "step": 2750
-    },
-    {
-      "epoch": 0.57,
-      "eval_accuracy": 0.33627769056340484,
-      "eval_loss": 4.24609375,
-      "eval_runtime": 6.5953,
-      "eval_samples_per_second": 5.307,
-      "eval_steps_per_second": 0.303,
-      "step": 2750
-    },
-    {
-      "epoch": 0.57,
-      "learning_rate": 9.142485924642703e-05,
-      "loss": 3.375,
-      "step": 2760
-    },
-    {
-      "epoch": 0.57,
-      "learning_rate": 9.099177132957992e-05,
-      "loss": 3.4365,
-      "step": 2770
-    },
-    {
-      "epoch": 0.57,
-      "learning_rate": 9.055868341273279e-05,
-      "loss": 3.4068,
-      "step": 2780
-    },
-    {
-      "epoch": 0.57,
-      "learning_rate": 9.012559549588567e-05,
-      "loss": 3.4333,
-      "step": 2790
-    },
-    {
-      "epoch": 0.58,
-      "learning_rate": 8.969250757903855e-05,
-      "loss": 3.3757,
-      "step": 2800
-    },
-    {
-      "epoch": 0.58,
-      "eval_accuracy": 0.33639630211058785,
-      "eval_loss": 4.24609375,
-      "eval_runtime": 6.5987,
-      "eval_samples_per_second": 5.304,
-      "eval_steps_per_second": 0.303,
-      "step": 2800
-    },
-    {
-      "epoch": 0.58,
-      "learning_rate": 8.925941966219143e-05,
-      "loss": 3.4111,
-      "step": 2810
-    },
-    {
-      "epoch": 0.58,
-      "learning_rate": 8.882633174534431e-05,
-      "loss": 3.4143,
-      "step": 2820
-    },
-    {
-      "epoch": 0.58,
-      "learning_rate": 8.839324382849718e-05,
-      "loss": 3.4307,
-      "step": 2830
-    },
-    {
-      "epoch": 0.58,
-      "learning_rate": 8.796015591165006e-05,
-      "loss": 3.3366,
-      "step": 2840
-    },
-    {
-      "epoch": 0.59,
-      "learning_rate": 8.752706799480295e-05,
-      "loss": 3.407,
-      "step": 2850
-    },
-    {
-      "epoch": 0.59,
-      "eval_accuracy": 0.33731728588871446,
-      "eval_loss": 4.234375,
-      "eval_runtime": 6.6038,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 2850
-    },
-    {
-      "epoch": 0.59,
-      "learning_rate": 8.709398007795583e-05,
-      "loss": 3.3506,
-      "step": 2860
-    },
-    {
-      "epoch": 0.59,
-      "learning_rate": 8.66608921611087e-05,
-      "loss": 3.429,
-      "step": 2870
-    },
-    {
-      "epoch": 0.59,
-      "learning_rate": 8.622780424426159e-05,
-      "loss": 3.338,
-      "step": 2880
-    },
-    {
-      "epoch": 0.59,
-      "learning_rate": 8.579471632741447e-05,
-      "loss": 3.4252,
-      "step": 2890
-    },
-    {
-      "epoch": 0.6,
-      "learning_rate": 8.536162841056736e-05,
-      "loss": 3.3986,
-      "step": 2900
-    },
-    {
-      "epoch": 0.6,
-      "eval_accuracy": 0.33657770800627945,
-      "eval_loss": 4.23828125,
-      "eval_runtime": 6.5874,
-      "eval_samples_per_second": 5.313,
-      "eval_steps_per_second": 0.304,
-      "step": 2900
-    },
-    {
-      "epoch": 0.6,
-      "learning_rate": 8.492854049372023e-05,
-      "loss": 3.4023,
-      "step": 2910
-    },
-    {
-      "epoch": 0.6,
-      "learning_rate": 8.449545257687311e-05,
-      "loss": 3.3919,
-      "step": 2920
-    },
-    {
-      "epoch": 0.6,
-      "learning_rate": 8.4062364660026e-05,
-      "loss": 3.3691,
-      "step": 2930
-    },
-    {
-      "epoch": 0.6,
-      "learning_rate": 8.362927674317888e-05,
-      "loss": 3.4017,
-      "step": 2940
-    },
-    {
-      "epoch": 0.61,
-      "learning_rate": 8.319618882633175e-05,
-      "loss": 3.4311,
-      "step": 2950
-    },
-    {
-      "epoch": 0.61,
-      "eval_accuracy": 0.3370940170940171,
-      "eval_loss": 4.234375,
-      "eval_runtime": 6.5922,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 2950
-    },
-    {
-      "epoch": 0.61,
-      "learning_rate": 8.276310090948462e-05,
-      "loss": 3.4224,
-      "step": 2960
-    },
-    {
-      "epoch": 0.61,
-      "learning_rate": 8.23300129926375e-05,
-      "loss": 3.3781,
-      "step": 2970
-    },
-    {
-      "epoch": 0.61,
-      "learning_rate": 8.189692507579039e-05,
-      "loss": 3.383,
-      "step": 2980
-    },
-    {
-      "epoch": 0.61,
-      "learning_rate": 8.146383715894327e-05,
-      "loss": 3.4332,
-      "step": 2990
-    },
-    {
-      "epoch": 0.62,
-      "learning_rate": 8.103074924209616e-05,
-      "loss": 3.3716,
-      "step": 3000
-    },
-    {
-      "epoch": 0.62,
-      "eval_accuracy": 0.33713587999302286,
-      "eval_loss": 4.234375,
-      "eval_runtime": 6.5875,
-      "eval_samples_per_second": 5.313,
-      "eval_steps_per_second": 0.304,
-      "step": 3000
-    },
-    {
-      "epoch": 0.62,
-      "learning_rate": 8.059766132524903e-05,
-      "loss": 3.4123,
-      "step": 3010
-    },
-    {
-      "epoch": 0.62,
-      "learning_rate": 8.016457340840191e-05,
-      "loss": 3.4181,
-      "step": 3020
-    },
-    {
-      "epoch": 0.62,
-      "learning_rate": 7.97314854915548e-05,
-      "loss": 3.3851,
-      "step": 3030
-    },
-    {
-      "epoch": 0.63,
-      "learning_rate": 7.929839757470768e-05,
-      "loss": 3.4224,
-      "step": 3040
-    },
-    {
-      "epoch": 0.63,
-      "learning_rate": 7.886530965786055e-05,
-      "loss": 3.3831,
-      "step": 3050
-    },
-    {
-      "epoch": 0.63,
-      "eval_accuracy": 0.33774986917844063,
-      "eval_loss": 4.23046875,
-      "eval_runtime": 6.5895,
-      "eval_samples_per_second": 5.311,
-      "eval_steps_per_second": 0.304,
-      "step": 3050
-    },
-    {
-      "epoch": 0.63,
-      "learning_rate": 7.843222174101343e-05,
-      "loss": 3.3965,
-      "step": 3060
-    },
-    {
-      "epoch": 0.63,
-      "learning_rate": 7.799913382416632e-05,
-      "loss": 3.4016,
-      "step": 3070
-    },
-    {
-      "epoch": 0.63,
-      "learning_rate": 7.756604590731919e-05,
-      "loss": 3.4047,
-      "step": 3080
-    },
-    {
-      "epoch": 0.64,
-      "learning_rate": 7.713295799047207e-05,
-      "loss": 3.3618,
-      "step": 3090
-    },
-    {
-      "epoch": 0.64,
-      "learning_rate": 7.669987007362494e-05,
-      "loss": 3.375,
-      "step": 3100
-    },
-    {
-      "epoch": 0.64,
-      "eval_accuracy": 0.3377638234781092,
-      "eval_loss": 4.23046875,
-      "eval_runtime": 6.5887,
-      "eval_samples_per_second": 5.312,
-      "eval_steps_per_second": 0.304,
-      "step": 3100
-    },
-    {
-      "epoch": 0.64,
-      "learning_rate": 7.626678215677783e-05,
-      "loss": 3.3899,
-      "step": 3110
-    },
-    {
-      "epoch": 0.64,
-      "learning_rate": 7.583369423993071e-05,
-      "loss": 3.3723,
-      "step": 3120
-    },
-    {
-      "epoch": 0.64,
-      "learning_rate": 7.54006063230836e-05,
-      "loss": 3.381,
-      "step": 3130
-    },
-    {
-      "epoch": 0.65,
-      "learning_rate": 7.496751840623647e-05,
-      "loss": 3.3558,
-      "step": 3140
-    },
-    {
-      "epoch": 0.65,
-      "learning_rate": 7.453443048938935e-05,
-      "loss": 3.3677,
-      "step": 3150
-    },
-    {
-      "epoch": 0.65,
-      "eval_accuracy": 0.33846851561137276,
-      "eval_loss": 4.21875,
-      "eval_runtime": 6.5962,
-      "eval_samples_per_second": 5.306,
-      "eval_steps_per_second": 0.303,
-      "step": 3150
-    },
-    {
-      "epoch": 0.65,
-      "learning_rate": 7.410134257254223e-05,
-      "loss": 3.3414,
-      "step": 3160
-    },
-    {
-      "epoch": 0.65,
-      "learning_rate": 7.366825465569512e-05,
-      "loss": 3.4144,
-      "step": 3170
-    },
-    {
-      "epoch": 0.65,
-      "learning_rate": 7.323516673884799e-05,
-      "loss": 3.3503,
-      "step": 3180
-    },
-    {
-      "epoch": 0.66,
-      "learning_rate": 7.280207882200087e-05,
-      "loss": 3.3716,
-      "step": 3190
-    },
-    {
-      "epoch": 0.66,
-      "learning_rate": 7.236899090515376e-05,
-      "loss": 3.3968,
-      "step": 3200
-    },
-    {
-      "epoch": 0.66,
-      "eval_accuracy": 0.3386220129077272,
-      "eval_loss": 4.22265625,
-      "eval_runtime": 6.5929,
-      "eval_samples_per_second": 5.309,
-      "eval_steps_per_second": 0.303,
-      "step": 3200
-    },
-    {
-      "epoch": 0.66,
-      "learning_rate": 7.193590298830663e-05,
-      "loss": 3.3485,
-      "step": 3210
-    },
-    {
-      "epoch": 0.66,
-      "learning_rate": 7.150281507145951e-05,
-      "loss": 3.4172,
-      "step": 3220
-    },
-    {
-      "epoch": 0.66,
-      "learning_rate": 7.106972715461238e-05,
-      "loss": 3.3727,
-      "step": 3230
-    },
-    {
-      "epoch": 0.67,
-      "learning_rate": 7.063663923776527e-05,
-      "loss": 3.3616,
-      "step": 3240
-    },
-    {
-      "epoch": 0.67,
-      "learning_rate": 7.020355132091815e-05,
-      "loss": 3.4069,
-      "step": 3250
-    },
-    {
-      "epoch": 0.67,
-      "eval_accuracy": 0.3380429094714809,
-      "eval_loss": 4.21875,
-      "eval_runtime": 6.5865,
-      "eval_samples_per_second": 5.314,
-      "eval_steps_per_second": 0.304,
-      "step": 3250
-    },
-    {
-      "epoch": 0.67,
-      "learning_rate": 6.977046340407103e-05,
-      "loss": 3.3583,
-      "step": 3260
-    },
-    {
-      "epoch": 0.67,
-      "learning_rate": 6.93373754872239e-05,
-      "loss": 3.3753,
-      "step": 3270
-    },
-    {
-      "epoch": 0.67,
-      "learning_rate": 6.890428757037679e-05,
-      "loss": 3.3443,
-      "step": 3280
-    },
-    {
-      "epoch": 0.68,
-      "learning_rate": 6.847119965352967e-05,
-      "loss": 3.3682,
-      "step": 3290
-    },
-    {
-      "epoch": 0.68,
-      "learning_rate": 6.803811173668256e-05,
-      "loss": 3.4192,
-      "step": 3300
-    },
-    {
-      "epoch": 0.68,
-      "eval_accuracy": 0.33877551020408164,
-      "eval_loss": 4.21484375,
-      "eval_runtime": 6.6033,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 3300
-    },
-    {
-      "epoch": 0.68,
-      "learning_rate": 6.760502381983543e-05,
-      "loss": 3.3657,
-      "step": 3310
-    },
-    {
-      "epoch": 0.68,
-      "learning_rate": 6.717193590298831e-05,
-      "loss": 3.3773,
-      "step": 3320
-    },
-    {
-      "epoch": 0.68,
-      "learning_rate": 6.67388479861412e-05,
-      "loss": 3.3604,
-      "step": 3330
-    },
-    {
-      "epoch": 0.69,
-      "learning_rate": 6.630576006929407e-05,
-      "loss": 3.404,
-      "step": 3340
-    },
-    {
-      "epoch": 0.69,
-      "learning_rate": 6.587267215244695e-05,
-      "loss": 3.3881,
-      "step": 3350
-    },
-    {
-      "epoch": 0.69,
-      "eval_accuracy": 0.33830804116518404,
-      "eval_loss": 4.21484375,
-      "eval_runtime": 6.6138,
-      "eval_samples_per_second": 5.292,
-      "eval_steps_per_second": 0.302,
-      "step": 3350
-    },
-    {
-      "epoch": 0.69,
-      "learning_rate": 6.543958423559982e-05,
-      "loss": 3.3855,
-      "step": 3360
-    },
-    {
-      "epoch": 0.69,
-      "learning_rate": 6.50064963187527e-05,
-      "loss": 3.314,
-      "step": 3370
-    },
-    {
-      "epoch": 0.7,
-      "learning_rate": 6.457340840190559e-05,
-      "loss": 3.4034,
-      "step": 3380
-    },
-    {
-      "epoch": 0.7,
-      "learning_rate": 6.414032048505847e-05,
-      "loss": 3.3969,
-      "step": 3390
-    },
-    {
-      "epoch": 0.7,
-      "learning_rate": 6.370723256821134e-05,
-      "loss": 3.3858,
-      "step": 3400
-    },
-    {
-      "epoch": 0.7,
-      "eval_accuracy": 0.33836385836385835,
-      "eval_loss": 4.2109375,
-      "eval_runtime": 6.6099,
-      "eval_samples_per_second": 5.295,
-      "eval_steps_per_second": 0.303,
-      "step": 3400
-    },
-    {
-      "epoch": 0.7,
-      "learning_rate": 6.327414465136423e-05,
-      "loss": 3.388,
-      "step": 3410
-    },
-    {
-      "epoch": 0.7,
-      "learning_rate": 6.284105673451711e-05,
-      "loss": 3.3832,
-      "step": 3420
-    },
-    {
-      "epoch": 0.71,
-      "learning_rate": 6.240796881767e-05,
-      "loss": 3.3098,
-      "step": 3430
-    },
-    {
-      "epoch": 0.71,
-      "learning_rate": 6.197488090082287e-05,
-      "loss": 3.3794,
-      "step": 3440
-    },
-    {
-      "epoch": 0.71,
-      "learning_rate": 6.154179298397575e-05,
-      "loss": 3.3999,
-      "step": 3450
-    },
-    {
-      "epoch": 0.71,
-      "eval_accuracy": 0.33883132740275596,
-      "eval_loss": 4.2109375,
-      "eval_runtime": 6.6039,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 3450
-    },
-    {
-      "epoch": 0.71,
-      "learning_rate": 6.110870506712864e-05,
-      "loss": 3.3278,
-      "step": 3460
-    },
-    {
-      "epoch": 0.71,
-      "learning_rate": 6.0675617150281506e-05,
-      "loss": 3.3645,
-      "step": 3470
-    },
-    {
-      "epoch": 0.72,
-      "learning_rate": 6.024252923343438e-05,
-      "loss": 3.3563,
-      "step": 3480
-    },
-    {
-      "epoch": 0.72,
-      "learning_rate": 5.985275010827198e-05,
-      "loss": 3.358,
-      "step": 3490
-    },
-    {
-      "epoch": 0.72,
-      "learning_rate": 5.9419662191424864e-05,
-      "loss": 3.3907,
-      "step": 3500
-    },
-    {
-      "epoch": 0.72,
-      "eval_accuracy": 0.33894296180010464,
-      "eval_loss": 4.2109375,
-      "eval_runtime": 6.6054,
-      "eval_samples_per_second": 5.299,
-      "eval_steps_per_second": 0.303,
-      "step": 3500
-    },
-    {
-      "epoch": 0.72,
-      "learning_rate": 5.898657427457774e-05,
-      "loss": 3.4023,
-      "step": 3510
-    },
-    {
-      "epoch": 0.72,
-      "learning_rate": 5.8553486357730626e-05,
-      "loss": 3.3519,
-      "step": 3520
-    },
-    {
-      "epoch": 0.73,
-      "learning_rate": 5.81203984408835e-05,
-      "loss": 3.3514,
-      "step": 3530
-    },
-    {
-      "epoch": 0.73,
-      "learning_rate": 5.768731052403639e-05,
-      "loss": 3.3151,
-      "step": 3540
-    },
-    {
-      "epoch": 0.73,
-      "learning_rate": 5.7254222607189265e-05,
-      "loss": 3.3929,
-      "step": 3550
-    },
-    {
-      "epoch": 0.73,
-      "eval_accuracy": 0.33893598465027036,
-      "eval_loss": 4.2109375,
-      "eval_runtime": 6.5935,
-      "eval_samples_per_second": 5.308,
-      "eval_steps_per_second": 0.303,
-      "step": 3550
-    },
-    {
-      "epoch": 0.73,
-      "learning_rate": 5.6821134690342135e-05,
-      "loss": 3.3983,
-      "step": 3560
-    },
-    {
-      "epoch": 0.73,
-      "learning_rate": 5.638804677349502e-05,
-      "loss": 3.3281,
-      "step": 3570
-    },
-    {
-      "epoch": 0.74,
-      "learning_rate": 5.59549588566479e-05,
-      "loss": 3.3654,
-      "step": 3580
-    },
-    {
-      "epoch": 0.74,
-      "learning_rate": 5.552187093980078e-05,
-      "loss": 3.379,
-      "step": 3590
-    },
-    {
-      "epoch": 0.74,
-      "learning_rate": 5.508878302295366e-05,
-      "loss": 3.3738,
-      "step": 3600
-    },
-    {
-      "epoch": 0.74,
-      "eval_accuracy": 0.33964067678353393,
-      "eval_loss": 4.20703125,
-      "eval_runtime": 6.6063,
-      "eval_samples_per_second": 5.298,
-      "eval_steps_per_second": 0.303,
-      "step": 3600
-    },
-    {
-      "epoch": 0.74,
-      "learning_rate": 5.465569510610654e-05,
-      "loss": 3.3727,
-      "step": 3610
-    },
-    {
-      "epoch": 0.74,
-      "learning_rate": 5.426591598094414e-05,
-      "loss": 3.3401,
-      "step": 3620
-    },
-    {
-      "epoch": 0.75,
-      "learning_rate": 5.3832828064097017e-05,
-      "loss": 3.3583,
-      "step": 3630
-    },
-    {
-      "epoch": 0.75,
-      "learning_rate": 5.33997401472499e-05,
-      "loss": 3.3868,
-      "step": 3640
-    },
-    {
-      "epoch": 0.75,
-      "learning_rate": 5.296665223040277e-05,
-      "loss": 3.3839,
-      "step": 3650
-    },
-    {
-      "epoch": 0.75,
-      "eval_accuracy": 0.3392918192918193,
-      "eval_loss": 4.20703125,
-      "eval_runtime": 6.5952,
-      "eval_samples_per_second": 5.307,
-      "eval_steps_per_second": 0.303,
-      "step": 3650
-    },
-    {
-      "epoch": 0.75,
-      "learning_rate": 5.253356431355565e-05,
-      "loss": 3.3713,
-      "step": 3660
-    },
-    {
-      "epoch": 0.75,
-      "learning_rate": 5.210047639670853e-05,
-      "loss": 3.3649,
-      "step": 3670
-    },
-    {
-      "epoch": 0.76,
-      "learning_rate": 5.166738847986141e-05,
-      "loss": 3.3836,
-      "step": 3680
-    },
-    {
-      "epoch": 0.76,
-      "learning_rate": 5.1234300563014294e-05,
-      "loss": 3.3227,
-      "step": 3690
-    },
-    {
-      "epoch": 0.76,
-      "learning_rate": 5.080121264616717e-05,
-      "loss": 3.3854,
-      "step": 3700
-    },
-    {
-      "epoch": 0.76,
-      "eval_accuracy": 0.3399546485260771,
-      "eval_loss": 4.20703125,
-      "eval_runtime": 6.6101,
-      "eval_samples_per_second": 5.295,
-      "eval_steps_per_second": 0.303,
-      "step": 3700
-    },
-    {
-      "epoch": 0.76,
-      "learning_rate": 5.0368124729320056e-05,
-      "loss": 3.3536,
-      "step": 3710
-    },
-    {
-      "epoch": 0.77,
-      "learning_rate": 4.993503681247293e-05,
-      "loss": 3.3943,
-      "step": 3720
-    },
-    {
-      "epoch": 0.77,
-      "learning_rate": 4.950194889562582e-05,
-      "loss": 3.3705,
-      "step": 3730
-    },
-    {
-      "epoch": 0.77,
-      "learning_rate": 4.9068860978778694e-05,
-      "loss": 3.3525,
-      "step": 3740
-    },
-    {
-      "epoch": 0.77,
-      "learning_rate": 4.863577306193157e-05,
-      "loss": 3.297,
-      "step": 3750
-    },
-    {
-      "epoch": 0.77,
-      "eval_accuracy": 0.339236002093145,
-      "eval_loss": 4.20703125,
-      "eval_runtime": 6.5942,
-      "eval_samples_per_second": 5.308,
-      "eval_steps_per_second": 0.303,
-      "step": 3750
-    },
-    {
-      "epoch": 0.77,
-      "learning_rate": 4.8202685145084456e-05,
-      "loss": 3.3305,
-      "step": 3760
-    },
-    {
-      "epoch": 0.78,
-      "learning_rate": 4.776959722823733e-05,
-      "loss": 3.3789,
-      "step": 3770
-    },
-    {
-      "epoch": 0.78,
-      "learning_rate": 4.733650931139022e-05,
-      "loss": 3.4127,
-      "step": 3780
-    },
-    {
-      "epoch": 0.78,
-      "learning_rate": 4.6903421394543095e-05,
-      "loss": 3.3234,
-      "step": 3790
-    },
-    {
-      "epoch": 0.78,
-      "learning_rate": 4.647033347769598e-05,
-      "loss": 3.2951,
-      "step": 3800
-    },
-    {
-      "epoch": 0.78,
-      "eval_accuracy": 0.3394732251875109,
-      "eval_loss": 4.203125,
-      "eval_runtime": 6.5899,
-      "eval_samples_per_second": 5.311,
-      "eval_steps_per_second": 0.303,
-      "step": 3800
-    },
-    {
-      "epoch": 0.78,
-      "learning_rate": 4.603724556084885e-05,
-      "loss": 3.3538,
-      "step": 3810
-    },
-    {
-      "epoch": 0.79,
-      "learning_rate": 4.5604157644001733e-05,
-      "loss": 3.4101,
-      "step": 3820
-    },
-    {
-      "epoch": 0.79,
-      "learning_rate": 4.517106972715461e-05,
-      "loss": 3.3232,
-      "step": 3830
-    },
-    {
-      "epoch": 0.79,
-      "learning_rate": 4.4737981810307495e-05,
-      "loss": 3.3519,
-      "step": 3840
-    },
-    {
-      "epoch": 0.79,
-      "learning_rate": 4.430489389346037e-05,
-      "loss": 3.3587,
-      "step": 3850
-    },
-    {
-      "epoch": 0.79,
-      "eval_accuracy": 0.3401709401709402,
-      "eval_loss": 4.19921875,
-      "eval_runtime": 6.5915,
-      "eval_samples_per_second": 5.31,
-      "eval_steps_per_second": 0.303,
-      "step": 3850
-    },
-    {
-      "epoch": 0.79,
-      "learning_rate": 4.3871805976613256e-05,
-      "loss": 3.3519,
-      "step": 3860
-    },
-    {
-      "epoch": 0.8,
-      "learning_rate": 4.3438718059766134e-05,
-      "loss": 3.3635,
-      "step": 3870
-    },
-    {
-      "epoch": 0.8,
-      "learning_rate": 4.300563014291901e-05,
-      "loss": 3.4013,
-      "step": 3880
-    },
-    {
-      "epoch": 0.8,
-      "learning_rate": 4.2572542226071895e-05,
-      "loss": 3.3224,
-      "step": 3890
-    },
-    {
-      "epoch": 0.8,
-      "learning_rate": 4.213945430922477e-05,
-      "loss": 3.3237,
-      "step": 3900
-    },
-    {
-      "epoch": 0.8,
-      "eval_accuracy": 0.33935461364032793,
-      "eval_loss": 4.203125,
-      "eval_runtime": 6.5894,
-      "eval_samples_per_second": 5.312,
-      "eval_steps_per_second": 0.304,
-      "step": 3900
-    },
-    {
-      "epoch": 0.8,
-      "learning_rate": 4.170636639237766e-05,
-      "loss": 3.3706,
-      "step": 3910
-    },
-    {
-      "epoch": 0.81,
-      "learning_rate": 4.1273278475530534e-05,
-      "loss": 3.3024,
-      "step": 3920
-    },
-    {
-      "epoch": 0.81,
-      "learning_rate": 4.084019055868342e-05,
-      "loss": 3.3717,
-      "step": 3930
-    },
-    {
-      "epoch": 0.81,
-      "learning_rate": 4.0407102641836295e-05,
-      "loss": 3.35,
-      "step": 3940
-    },
-    {
-      "epoch": 0.81,
-      "learning_rate": 3.997401472498917e-05,
-      "loss": 3.3136,
-      "step": 3950
-    },
-    {
-      "epoch": 0.81,
-      "eval_accuracy": 0.3393267050409908,
-      "eval_loss": 4.203125,
-      "eval_runtime": 6.6033,
-      "eval_samples_per_second": 5.3,
-      "eval_steps_per_second": 0.303,
-      "step": 3950
-    },
-    {
-      "epoch": 0.81,
-      "learning_rate": 3.954092680814206e-05,
-      "loss": 3.3281,
-      "step": 3960
-    },
-    {
-      "epoch": 0.82,
-      "learning_rate": 3.9107838891294934e-05,
-      "loss": 3.2916,
-      "step": 3970
-    },
-    {
-      "epoch": 0.82,
-      "learning_rate": 3.867475097444782e-05,
-      "loss": 3.3578,
-      "step": 3980
-    },
-    {
-      "epoch": 0.82,
-      "learning_rate": 3.8241663057600696e-05,
-      "loss": 3.3789,
-      "step": 3990
-    },
-    {
-      "epoch": 0.82,
-      "learning_rate": 3.780857514075358e-05,
-      "loss": 3.3367,
-      "step": 4000
-    },
-    {
-      "epoch": 0.82,
-      "eval_accuracy": 0.33942438513867085,
-      "eval_loss": 4.203125,
-      "eval_runtime": 6.5852,
-      "eval_samples_per_second": 5.315,
-      "eval_steps_per_second": 0.304,
-      "step": 4000
-    }
-  ],
-  "max_steps": 4862,
-  "num_train_epochs": 1,
-  "total_flos": 4.250832212066304e+19,
-  "trial_name": null,
-  "trial_params": null
-}

checkpoint-4000/training_args.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8882a918bcb9a315dfcc77f1a3a879de69b8b68f463ac4b04ba7f0c085598f2d
-size 5115

checkpoint-4000/zero_to_fp32.py

@@ -1,578 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright (c) Microsoft Corporation.
-# SPDX-License-Identifier: Apache-2.0
-
-# DeepSpeed Team
-
-# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
-# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
-# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
-# application.
-#
-# example: python zero_to_fp32.py . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-from dataclasses import dataclass
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
-                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
-                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
-
-
-@dataclass
-class zero_model_state:
-    buffers: dict()
-    param_shapes: dict()
-    shared_params: list
-    ds_version: int
-    frozen_param_shapes: dict()
-    frozen_param_fragments: dict()
-
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
-    return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
-    '''
-    alist.sort(key=natural_keys) sorts in human order
-    http://nedbatchelder.com/blog/200712/human_sorting.html
-    (See Toothy's implementation in the comments)
-    '''
-    return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
-    if not os.path.isdir(checkpoint_dir):
-        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
-    # there should be only one file
-    if zero_stage == 2:
-        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
-    elif zero_stage == 3:
-        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
-    if not os.path.exists(file):
-        raise FileNotFoundError(f"can't find model states file at '{file}'")
-
-    return file
-
-
-def get_checkpoint_files(checkpoint_dir, glob_pattern):
-    # XXX: need to test that this simple glob rule works for multi-node setup too
-    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
-
-    if len(ckpt_files) == 0:
-        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
-
-    return ckpt_files
-
-
-def get_optim_files(checkpoint_dir):
-    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
-
-
-def get_model_state_files(checkpoint_dir):
-    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
-
-
-def parse_model_states(files):
-    zero_model_states = []
-    for file in files:
-        state_dict = torch.load(file, map_location=device)
-
-        if BUFFER_NAMES not in state_dict:
-            raise ValueError(f"{file} is not a model state checkpoint")
-        buffer_names = state_dict[BUFFER_NAMES]
-        if debug:
-            print("Found buffers:", buffer_names)
-
-        # recover just the buffers while restoring them to fp32 if they were saved in fp16
-        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
-        param_shapes = state_dict[PARAM_SHAPES]
-
-        # collect parameters that are included in param_shapes
-        param_names = []
-        for s in param_shapes:
-            for name in s.keys():
-                param_names.append(name)
-
-        # update with frozen parameters
-        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
-        if frozen_param_shapes is not None:
-            if debug:
-                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
-            param_names += list(frozen_param_shapes.keys())
-
-        # handle shared params
-        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
-
-        ds_version = state_dict.get(DS_VERSION, None)
-
-        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
-
-        z_model_state = zero_model_state(buffers=buffers,
-                                         param_shapes=param_shapes,
-                                         shared_params=shared_params,
-                                         ds_version=ds_version,
-                                         frozen_param_shapes=frozen_param_shapes,
-                                         frozen_param_fragments=frozen_param_fragments)
-        zero_model_states.append(z_model_state)
-
-    return zero_model_states
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
-    total_files = len(files)
-    state_dicts = []
-    for f in files:
-        state_dicts.append(torch.load(f, map_location=device))
-
-    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
-        raise ValueError(f"{files[0]} is not a zero checkpoint")
-    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
-    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
-    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
-    # parameters can be different from data parallelism for non-expert parameters. So we can just
-    # use the max of the partition_count to get the dp world_size.
-
-    if type(world_size) is list:
-        world_size = max(world_size)
-
-    if world_size != total_files:
-        raise ValueError(
-            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
-            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
-        )
-
-    # the groups are named differently in each stage
-    if zero_stage == 2:
-        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
-    elif zero_stage == 3:
-        fp32_groups_key = FP32_FLAT_GROUPS
-    else:
-        raise ValueError(f"unknown zero stage {zero_stage}")
-
-    if zero_stage == 2:
-        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
-    elif zero_stage == 3:
-        # if there is more than one param group, there will be multiple flattened tensors - one
-        # flattened tensor per group - for simplicity merge them into a single tensor
-        #
-        # XXX: could make the script more memory efficient for when there are multiple groups - it
-        # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
-        fp32_flat_groups = [
-            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
-        ]
-
-    return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
-    """
-    Returns fp32 state_dict reconstructed from ds checkpoint
-
-    Args:
-        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
-    """
-    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
-    optim_files = get_optim_files(ds_checkpoint_dir)
-    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
-    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
-    model_files = get_model_state_files(ds_checkpoint_dir)
-
-    zero_model_states = parse_model_states(model_files)
-    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
-
-    if zero_stage == 2:
-        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-    elif zero_stage == 3:
-        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-
-
-def _zero2_merge_frozen_params(state_dict, zero_model_states):
-    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
-        return
-
-    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
-    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
-
-    if debug:
-        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
-        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
-        wanted_params = len(frozen_param_shapes)
-        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
-        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
-        print(f'Frozen params: Have {avail_numel} numels to process.')
-        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
-    total_params = 0
-    total_numel = 0
-    for name, shape in frozen_param_shapes.items():
-        total_params += 1
-        unpartitioned_numel = shape.numel()
-        total_numel += unpartitioned_numel
-
-        state_dict[name] = frozen_param_fragments[name]
-
-        if debug:
-            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
-
-    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
-    param_shapes = zero_model_states[0].param_shapes
-
-    # Reconstruction protocol:
-    #
-    # XXX: document this
-
-    if debug:
-        for i in range(world_size):
-            for j in range(len(fp32_flat_groups[0])):
-                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
-
-    # XXX: memory usage doubles here (zero2)
-    num_param_groups = len(fp32_flat_groups[0])
-    merged_single_partition_of_fp32_groups = []
-    for i in range(num_param_groups):
-        merged_partitions = [sd[i] for sd in fp32_flat_groups]
-        full_single_fp32_vector = torch.cat(merged_partitions, 0)
-        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
-    avail_numel = sum(
-        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
-
-    if debug:
-        wanted_params = sum([len(shapes) for shapes in param_shapes])
-        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
-        # not asserting if there is a mismatch due to possible padding
-        print(f"Have {avail_numel} numels to process.")
-        print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
-    # params
-    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
-    # out-of-core computing solution
-    total_numel = 0
-    total_params = 0
-    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
-        offset = 0
-        avail_numel = full_single_fp32_vector.numel()
-        for name, shape in shapes.items():
-
-            unpartitioned_numel = shape.numel()
-            total_numel += unpartitioned_numel
-            total_params += 1
-
-            if debug:
-                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
-            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
-            offset += unpartitioned_numel
-
-        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
-        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
-        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
-        # live optimizer object, so we are checking that the numbers are within the right range
-        align_to = 2 * world_size
-
-        def zero2_align(x):
-            return align_to * math.ceil(x / align_to)
-
-        if debug:
-            print(f"original offset={offset}, avail_numel={avail_numel}")
-
-        offset = zero2_align(offset)
-        avail_numel = zero2_align(avail_numel)
-
-        if debug:
-            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
-
-        # Sanity check
-        if offset != avail_numel:
-            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
-    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states):
-    state_dict = OrderedDict()
-
-    # buffers
-    buffers = zero_model_states[0].buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    _zero2_merge_frozen_params(state_dict, zero_model_states)
-
-    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
-    # recover shared parameters
-    for pair in zero_model_states[0].shared_params:
-        if pair[1] in state_dict:
-            state_dict[pair[0]] = state_dict[pair[1]]
-
-    return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
-    remainder = unpartitioned_numel % world_size
-    padding_numel = (world_size - remainder) if remainder else 0
-    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
-    return partitioned_numel, padding_numel
-
-
-def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
-    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
-        return
-
-    if debug:
-        for i in range(world_size):
-            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
-            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
-
-        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
-        wanted_params = len(frozen_param_shapes)
-        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
-        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
-        print(f'Frozen params: Have {avail_numel} numels to process.')
-        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
-
-    total_params = 0
-    total_numel = 0
-    for name, shape in zero_model_states[0].frozen_param_shapes.items():
-        total_params += 1
-        unpartitioned_numel = shape.numel()
-        total_numel += unpartitioned_numel
-
-        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
-        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
-
-        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
-        if debug:
-            print(
-                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
-            )
-
-    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
-    param_shapes = zero_model_states[0].param_shapes
-    avail_numel = fp32_flat_groups[0].numel() * world_size
-    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
-    # param, re-consolidating each param, while dealing with padding if any
-
-    # merge list of dicts, preserving order
-    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
-    if debug:
-        for i in range(world_size):
-            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
-        wanted_params = len(param_shapes)
-        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
-        # not asserting if there is a mismatch due to possible padding
-        avail_numel = fp32_flat_groups[0].numel() * world_size
-        print(f"Trainable params: Have {avail_numel} numels to process.")
-        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
-
-    # params
-    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
-    # out-of-core computing solution
-    offset = 0
-    total_numel = 0
-    total_params = 0
-    for name, shape in param_shapes.items():
-
-        unpartitioned_numel = shape.numel()
-        total_numel += unpartitioned_numel
-        total_params += 1
-
-        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
-        if debug:
-            print(
-                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
-            )
-
-        # XXX: memory usage doubles here
-        state_dict[name] = torch.cat(
-            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
-            0).narrow(0, 0, unpartitioned_numel).view(shape)
-        offset += partitioned_numel
-
-    offset *= world_size
-
-    # Sanity check
-    if offset != avail_numel:
-        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
-    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
-    state_dict = OrderedDict()
-
-    # buffers
-    buffers = zero_model_states[0].buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
-
-    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
-
-    # recover shared parameters
-    for pair in zero_model_states[0].shared_params:
-        if pair[1] in state_dict:
-            state_dict[pair[0]] = state_dict[pair[1]]
-
-    return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
-    """
-    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
-    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
-    via a model hub.
-
-    Args:
-        - ``checkpoint_dir``: path to the desired checkpoint folder
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
-    Returns:
-        - pytorch ``state_dict``
-
-    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
-    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
-    the checkpoint.
-
-    A typical usage might be ::
-
-        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
-        # do the training and checkpoint saving
-        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
-        model = model.cpu() # move to cpu
-        model.load_state_dict(state_dict)
-        # submit to model hub or save the model to share with others
-
-    In this example the ``model`` will no longer be usable in the deepspeed context of the same
-    application. i.e. you will need to re-initialize the deepspeed engine, since
-    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
-    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
-    """
-    if tag is None:
-        latest_path = os.path.join(checkpoint_dir, 'latest')
-        if os.path.isfile(latest_path):
-            with open(latest_path, 'r') as fd:
-                tag = fd.read().strip()
-        else:
-            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
-    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
-    if not os.path.isdir(ds_checkpoint_dir):
-        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
-    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
-    """
-    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
-    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
-    Args:
-        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
-        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-    """
-
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-    print(f"Saving fp32 state dict to {output_file}")
-    torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
-    """
-    1. Put the provided model to cpu
-    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
-    3. Load it into the provided model
-
-    Args:
-        - ``model``: the model object to update
-        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
-    Returns:
-        - ``model`: modified model
-
-    Make sure you have plenty of CPU memory available before you call this function. If you don't
-    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
-    conveniently placed for you in the checkpoint folder.
-
-    A typical usage might be ::
-
-        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
-        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
-        # submit to model hub or save the model to share with others
-
-    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
-    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
-    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
-    """
-    logger.info(f"Extracting fp32 weights")
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
-    logger.info(f"Overwriting model with fp32 weights")
-    model = model.cpu()
-    model.load_state_dict(state_dict, strict=False)
-
-    return model
-
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("checkpoint_dir",
-                        type=str,
-                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
-    parser.add_argument(
-        "output_file",
-        type=str,
-        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
-    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
-    args = parser.parse_args()
-
-    debug = args.debug
-
-    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)