| W0824 23:13:21.569000 5193 torch/distributed/run.py:774] |
| W0824 23:13:21.569000 5193 torch/distributed/run.py:774] ***************************************** |
| W0824 23:13:21.569000 5193 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. |
| W0824 23:13:21.569000 5193 torch/distributed/run.py:774] ***************************************** |
| Overriding config with configs/training/minimal-en-125m-4ep.py: |
| |
|
|
| |
| out_dir = "../runs/minimal-en-125m-4ep" |
| eval_interval = 1000 |
| eval_iters = 100 |
| log_interval = 10 |
| always_save_checkpoint = True |
| init_from = "scratch" |
|
|
| |
| wandb_log = True |
| wandb_project = "minimal-en" |
| wandb_run_name = "125m-12k-4ep" |
|
|
| |
| dataset = "minimal-en-corpus-2.5b" |
| gradient_accumulation_steps = 40 |
| batch_size = 12 |
| block_size = 1024 |
|
|
| |
| n_layer = 12 |
| n_head = 14 |
| n_embd = 896 |
| dropout = 0.0 |
| bias = False |
|
|
| |
| learning_rate = 3e-4 |
| max_iters = 22003 |
| weight_decay = 0.1 |
| beta1 = 0.9 |
| beta2 = 0.95 |
| grad_clip = 1.0 |
|
|
| |
| decay_lr = True |
| warmup_iters = 440 |
| lr_decay_iters = 22003 |
| min_lr = 3e-5 |
|
|
| |
| backend = "nccl" |
| device = "cuda" |
| dtype = "bfloat16" |
| compile = True |
|
|
| Overriding config with configs/training/minimal-en-125m-4ep.py: |
| |
|
|
| |
| out_dir = "../runs/minimal-en-125m-4ep" |
| eval_interval = 1000 |
| eval_iters = 100 |
| log_interval = 10 |
| always_save_checkpoint = True |
| init_from = "scratch" |
|
|
| |
| wandb_log = True |
| wandb_project = "minimal-en" |
| wandb_run_name = "125m-12k-4ep" |
|
|
| |
| dataset = "minimal-en-corpus-2.5b" |
| gradient_accumulation_steps = 40 |
| batch_size = 12 |
| block_size = 1024 |
|
|
| |
| n_layer = 12 |
| n_head = 14 |
| n_embd = 896 |
| dropout = 0.0 |
| bias = False |
|
|
| |
| learning_rate = 3e-4 |
| max_iters = 22003 |
| weight_decay = 0.1 |
| beta1 = 0.9 |
| beta2 = 0.95 |
| grad_clip = 1.0 |
|
|
| |
| decay_lr = True |
| warmup_iters = 440 |
| lr_decay_iters = 22003 |
| min_lr = 3e-5 |
|
|
| |
| backend = "nccl" |
| device = "cuda" |
| dtype = "bfloat16" |
| compile = True |
|
|
| tokens per iteration will be: 491,520 |
| found vocab_size = 12288 (inside data/minimal-en-corpus-2.5b/meta.pkl) |
| Initializing a new model from scratch |
| tokens per iteration will be: 491,520 |
| found vocab_size = 12288 (inside data/minimal-en-corpus-2.5b/meta.pkl) |
| Initializing a new model from scratch |
| number of parameters: 126.64M |
| number of parameters: 126.64M |
| /workspace/nanogpt/train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) |
| num decayed parameter tensors: 50, with 127,533,056 parameters |
| num non-decayed parameter tensors: 25, with 22,400 parameters |
| /workspace/nanogpt/train.py:196: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) |
| num decayed parameter tensors: 50, with 127,533,056 parameters |
| num non-decayed parameter tensors: 25, with 22,400 parameters |
| using fused AdamW: True |
| compiling the model... (takes a ~minute) |
| using fused AdamW: True |
| compiling the model... (takes a ~minute) |
| NCCL version 2.27.3+cuda12.9 |
| wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc. |
| wandb: Currently logged in as: dawidmajewski (dawidmajewski-ezotic) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin |
| wandb: setting up run hlbpqpoe |
| wandb: Tracking run with wandb version 0.28.2 |
| wandb: Run data is saved locally in /workspace/logs/wandb/wandb/run-20260824_231331-hlbpqpoe |
| wandb: Run `wandb offline` to turn off syncing. |
| wandb: Syncing run 125m-12k-4ep |
| wandb: ⭐️ View project at https://wandb.ai/dawidmajewski-ezotic/minimal-en |
| wandb: 🚀 View run at https://wandb.ai/dawidmajewski-ezotic/minimal-en/runs/hlbpqpoe |
| step 0: train loss 9.5797, val loss 9.5732 |
| iter 0: loss 9.5701, time 36799.30ms, mfu -100.00% |
| iter 10: loss 8.6515, time 1993.32ms, mfu 35.25% |
| iter 20: loss 8.4448, time 2017.84ms, mfu 35.20% |
| iter 30: loss 8.2446, time 2039.18ms, mfu 35.13% |
| iter 40: loss 7.9333, time 2044.29ms, mfu 35.05% |
| iter 50: loss 7.8167, time 2061.04ms, mfu 34.96% |
| iter 60: loss 7.4723, time 2066.40ms, mfu 34.86% |
| iter 70: loss 7.1812, time 2067.45ms, mfu 34.77% |
| iter 80: loss 7.0585, time 2085.67ms, mfu 34.66% |
| iter 90: loss 6.8916, time 2071.91ms, mfu 34.59% |
| iter 100: loss 6.9671, time 2082.79ms, mfu 34.50% |
| iter 110: loss 6.6913, time 2083.09ms, mfu 34.43% |
| iter 120: loss 6.4896, time 2070.43ms, mfu 34.38% |
| iter 130: loss 6.4626, time 2069.20ms, mfu 34.33% |
| iter 140: loss 6.3726, time 2095.37ms, mfu 34.25% |
| iter 150: loss 6.2077, time 2093.91ms, mfu 34.18% |
| iter 160: loss 6.1422, time 2093.72ms, mfu 34.12% |
| iter 170: loss 5.9801, time 2101.12ms, mfu 34.05% |
| iter 180: loss 6.2115, time 2087.54ms, mfu 34.01% |
| iter 190: loss 5.8301, time 2103.76ms, mfu 33.95% |
| iter 200: loss 5.9559, time 2084.75ms, mfu 33.93% |
| iter 210: loss 5.8527, time 2090.91ms, mfu 33.89% |
| iter 220: loss 5.8517, time 2100.90ms, mfu 33.85% |
| iter 230: loss 5.6927, time 2080.43ms, mfu 33.84% |
| iter 240: loss 5.5679, time 2077.56ms, mfu 33.84% |
| iter 250: loss 5.6053, time 2106.90ms, mfu 33.79% |
| iter 260: loss 5.4759, time 2077.33ms, mfu 33.79% |
| iter 270: loss 5.3678, time 2076.06ms, mfu 33.80% |
| iter 280: loss 5.4428, time 2097.76ms, mfu 33.77% |
| iter 290: loss 5.2989, time 2086.06ms, mfu 33.76% |
| iter 300: loss 5.0415, time 2098.62ms, mfu 33.73% |
| iter 310: loss 4.9178, time 2078.58ms, mfu 33.74% |
| iter 320: loss 5.1031, time 2080.53ms, mfu 33.74% |
| iter 330: loss 5.3207, time 2104.51ms, mfu 33.70% |
| iter 340: loss 5.2468, time 2104.70ms, mfu 33.67% |
| iter 350: loss 5.2147, time 2086.30ms, mfu 33.67% |
| iter 360: loss 5.1747, time 2085.95ms, mfu 33.67% |
| iter 370: loss 5.0355, time 2079.08ms, mfu 33.69% |
| iter 380: loss 5.0886, time 2089.18ms, mfu 33.68% |
| iter 390: loss 5.0313, time 2095.34ms, mfu 33.67% |
| iter 400: loss 4.9797, time 2078.22ms, mfu 33.68% |
| iter 410: loss 5.0387, time 2069.48ms, mfu 33.71% |
| iter 420: loss 4.8122, time 2077.42ms, mfu 33.72% |
| iter 430: loss 5.1012, time 2091.23ms, mfu 33.71% |
| iter 440: loss 5.0285, time 2090.85ms, mfu 33.70% |
| iter 450: loss 4.7803, time 2085.61ms, mfu 33.69% |
| iter 460: loss 4.9936, time 2100.78ms, mfu 33.67% |
| iter 470: loss 4.2048, time 2130.72ms, mfu 33.60% |
| iter 480: loss 4.6369, time 2100.40ms, mfu 33.58% |
| iter 490: loss 4.6676, time 2104.42ms, mfu 33.56% |
| iter 500: loss 4.8083, time 2109.12ms, mfu 33.54% |
| iter 510: loss 4.4331, time 2074.72ms, mfu 33.57% |
| iter 520: loss 4.6347, time 2106.84ms, mfu 33.55% |
| iter 530: loss 4.7749, time 2081.79ms, mfu 33.57% |
| iter 540: loss 4.6217, time 2099.14ms, mfu 33.56% |
| iter 550: loss 4.4999, time 2101.37ms, mfu 33.55% |
| iter 560: loss 4.3056, time 2070.50ms, mfu 33.59% |
| iter 570: loss 4.4181, time 2104.49ms, mfu 33.57% |
| iter 580: loss 4.3402, time 2083.84ms, mfu 33.58% |
| iter 590: loss 4.5584, time 2103.17ms, mfu 33.56% |
| iter 600: loss 4.4462, time 2078.00ms, mfu 33.59% |
| iter 610: loss 4.2681, time 2104.35ms, mfu 33.57% |
| iter 620: loss 3.8997, time 2119.20ms, mfu 33.53% |
| iter 630: loss 4.1709, time 2084.69ms, mfu 33.54% |
| iter 640: loss 4.0962, time 2104.52ms, mfu 33.53% |
| iter 650: loss 3.9719, time 2088.32ms, mfu 33.54% |
| iter 660: loss 4.1625, time 2102.31ms, mfu 33.53% |
| iter 670: loss 4.0754, time 2086.50ms, mfu 33.54% |
| iter 680: loss 3.7406, time 2094.57ms, mfu 33.54% |
| iter 690: loss 3.9339, time 2085.20ms, mfu 33.56% |
| iter 700: loss 3.8437, time 2074.83ms, mfu 33.59% |
| iter 710: loss 3.9143, time 2098.15ms, mfu 33.58% |
| iter 720: loss 3.9684, time 2100.45ms, mfu 33.56% |
| iter 730: loss 3.5712, time 2098.23ms, mfu 33.56% |
| iter 740: loss 4.0678, time 2093.66ms, mfu 33.56% |
| iter 750: loss 4.0130, time 2084.70ms, mfu 33.57% |
| iter 760: loss 3.8476, time 2105.70ms, mfu 33.55% |
| iter 770: loss 3.9298, time 2086.56ms, mfu 33.56% |
| iter 780: loss 3.6444, time 2093.14ms, mfu 33.56% |
| iter 790: loss 3.5690, time 2090.08ms, mfu 33.57% |
| iter 800: loss 3.4821, time 2103.30ms, mfu 33.55% |
| iter 810: loss 3.7268, time 2082.37ms, mfu 33.57% |
| iter 820: loss 3.5891, time 2085.35ms, mfu 33.58% |
| iter 830: loss 3.7140, time 2083.72ms, mfu 33.60% |
| iter 840: loss 3.2428, time 2111.91ms, mfu 33.56% |
| iter 850: loss 3.3661, time 2104.27ms, mfu 33.55% |
| iter 860: loss 3.4586, time 2098.45ms, mfu 33.54% |
| iter 870: loss 3.6945, time 2090.80ms, mfu 33.55% |
| iter 880: loss 3.4731, time 2101.89ms, mfu 33.53% |
| iter 890: loss 3.5809, time 2074.95ms, mfu 33.57% |
| iter 900: loss 3.6004, time 2099.76ms, mfu 33.56% |
| iter 910: loss 3.5649, time 2095.65ms, mfu 33.55% |
| iter 920: loss 3.7382, time 2105.51ms, mfu 33.53% |
| iter 930: loss 3.5281, time 2083.98ms, mfu 33.55% |
| iter 940: loss 3.8531, time 2115.73ms, mfu 33.52% |
| iter 950: loss 3.6362, time 2108.05ms, mfu 33.50% |
| iter 960: loss 3.4495, time 2112.52ms, mfu 33.47% |
| iter 970: loss 3.5198, time 2104.74ms, mfu 33.47% |
| iter 980: loss 3.3078, time 2089.55ms, mfu 33.48% |
| iter 990: loss 3.4061, time 2092.22ms, mfu 33.49% |
| step 1000: train loss 3.4769, val loss 3.5219 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 1000: loss 3.3850, time 9006.11ms, mfu 30.92% |
| iter 1010: loss 3.2370, time 2078.63ms, mfu 31.21% |
| iter 1020: loss 3.3580, time 2100.29ms, mfu 31.43% |
| iter 1030: loss 3.6355, time 2092.06ms, mfu 31.65% |
| iter 1040: loss 3.2284, time 2087.74ms, mfu 31.85% |
| iter 1050: loss 3.3297, time 2097.62ms, mfu 32.01% |
| iter 1060: loss 3.2152, time 2086.47ms, mfu 32.18% |
| iter 1070: loss 3.5770, time 2076.36ms, mfu 32.35% |
| iter 1080: loss 3.4146, time 2106.38ms, mfu 32.45% |
| iter 1090: loss 2.9954, time 2085.45ms, mfu 32.57% |
| iter 1100: loss 3.6749, time 2109.49ms, mfu 32.64% |
| iter 1110: loss 3.2327, time 2106.02ms, mfu 32.72% |
| iter 1120: loss 3.2230, time 2088.08ms, mfu 32.81% |
| iter 1130: loss 3.4330, time 2106.66ms, mfu 32.86% |
| iter 1140: loss 3.2531, time 2090.60ms, mfu 32.94% |
| iter 1150: loss 3.3821, time 2092.74ms, mfu 33.00% |
| iter 1160: loss 3.4423, time 2105.26ms, mfu 33.04% |
| iter 1170: loss 3.3262, time 2087.35ms, mfu 33.10% |
| iter 1180: loss 3.5381, time 2091.57ms, mfu 33.15% |
| iter 1190: loss 3.2797, time 2110.25ms, mfu 33.16% |
| iter 1200: loss 3.3246, time 2075.26ms, mfu 33.23% |
| iter 1210: loss 3.4333, time 2094.02ms, mfu 33.26% |
| iter 1220: loss 3.4259, time 2088.27ms, mfu 33.30% |
| iter 1230: loss 3.2118, time 2091.79ms, mfu 33.33% |
| iter 1240: loss 2.9138, time 2097.08ms, mfu 33.35% |
| iter 1250: loss 3.2667, time 2088.42ms, mfu 33.38% |
| iter 1260: loss 2.9204, time 2099.43ms, mfu 33.39% |
| iter 1270: loss 3.0572, time 2086.39ms, mfu 33.42% |
| iter 1280: loss 3.1568, time 2117.32ms, mfu 33.39% |
| iter 1290: loss 3.4388, time 2086.31ms, mfu 33.42% |
| iter 1300: loss 3.1613, time 2086.44ms, mfu 33.45% |
| iter 1310: loss 3.4910, time 2088.56ms, mfu 33.47% |
| iter 1320: loss 3.1674, time 2080.59ms, mfu 33.50% |
| iter 1330: loss 3.0806, time 2086.68ms, mfu 33.51% |
| iter 1340: loss 3.3800, time 2108.42ms, mfu 33.49% |
| iter 1350: loss 3.3195, time 2089.45ms, mfu 33.51% |
| iter 1360: loss 3.5254, time 2096.98ms, mfu 33.51% |
| iter 1370: loss 3.3237, time 2108.79ms, mfu 33.49% |
| iter 1380: loss 3.2951, time 2104.57ms, mfu 33.48% |
| iter 1390: loss 3.2490, time 2087.63ms, mfu 33.50% |
| iter 1400: loss 3.2035, time 2099.25ms, mfu 33.49% |
| iter 1410: loss 3.2028, time 2096.14ms, mfu 33.49% |
| iter 1420: loss 2.9834, time 2091.12ms, mfu 33.51% |
| iter 1430: loss 3.1010, time 2106.93ms, mfu 33.49% |
| iter 1440: loss 3.0424, time 2083.61ms, mfu 33.51% |
| iter 1450: loss 2.9155, time 2098.67ms, mfu 33.51% |
| iter 1460: loss 3.1797, time 2084.02ms, mfu 33.53% |
| iter 1470: loss 3.1364, time 2088.68ms, mfu 33.54% |
| iter 1480: loss 3.5031, time 2089.95ms, mfu 33.55% |
| iter 1490: loss 3.3129, time 2098.83ms, mfu 33.54% |
| iter 1500: loss 3.3140, time 2078.42ms, mfu 33.57% |
| iter 1510: loss 3.3269, time 2096.52ms, mfu 33.56% |
| iter 1520: loss 3.1147, time 2094.67ms, mfu 33.56% |
| iter 1530: loss 3.5681, time 2081.67ms, mfu 33.58% |
| iter 1540: loss 3.3100, time 2091.29ms, mfu 33.58% |
| iter 1550: loss 3.1636, time 2082.59ms, mfu 33.60% |
| iter 1560: loss 3.2076, time 2081.72ms, mfu 33.61% |
| iter 1570: loss 3.2339, time 2082.92ms, mfu 33.62% |
| iter 1580: loss 2.9632, time 2081.89ms, mfu 33.64% |
| iter 1590: loss 3.2501, time 2090.25ms, mfu 33.63% |
| iter 1600: loss 3.0978, time 2086.36ms, mfu 33.64% |
| iter 1610: loss 3.0183, time 2095.82ms, mfu 33.63% |
| iter 1620: loss 3.2033, time 2103.42ms, mfu 33.60% |
| iter 1630: loss 3.3264, time 2082.80ms, mfu 33.62% |
| iter 1640: loss 2.8962, time 2080.24ms, mfu 33.63% |
| iter 1650: loss 3.2974, time 2082.40ms, mfu 33.64% |
| iter 1660: loss 3.1878, time 2077.11ms, mfu 33.66% |
| iter 1670: loss 2.9475, time 2088.90ms, mfu 33.66% |
| iter 1680: loss 3.1013, time 2079.50ms, mfu 33.67% |
| iter 1690: loss 3.0734, time 2073.39ms, mfu 33.69% |
| iter 1700: loss 3.5066, time 2079.11ms, mfu 33.70% |
| iter 1710: loss 3.1908, time 2088.10ms, mfu 33.70% |
| iter 1720: loss 3.2536, time 2108.03ms, mfu 33.66% |
| iter 1730: loss 3.2540, time 2107.71ms, mfu 33.63% |
| iter 1740: loss 2.9262, time 2088.94ms, mfu 33.63% |
| iter 1750: loss 3.2834, time 2091.66ms, mfu 33.62% |
| iter 1760: loss 3.4747, time 2087.66ms, mfu 33.63% |
| iter 1770: loss 3.2402, time 2081.42ms, mfu 33.64% |
| iter 1780: loss 2.9697, time 2075.73ms, mfu 33.66% |
| iter 1790: loss 2.9834, time 2087.18ms, mfu 33.66% |
| iter 1800: loss 3.1758, time 2095.63ms, mfu 33.65% |
| iter 1810: loss 3.0526, time 2105.01ms, mfu 33.62% |
| iter 1820: loss 2.9602, time 2089.51ms, mfu 33.62% |
| iter 1830: loss 2.8927, time 2103.48ms, mfu 33.60% |
| iter 1840: loss 3.2547, time 2103.57ms, mfu 33.58% |
| iter 1850: loss 3.1535, time 2078.31ms, mfu 33.60% |
| iter 1860: loss 3.1056, time 2091.17ms, mfu 33.60% |
| iter 1870: loss 3.2162, time 2073.97ms, mfu 33.63% |
| iter 1880: loss 3.0082, time 2083.10ms, mfu 33.64% |
| iter 1890: loss 3.0484, time 2075.17ms, mfu 33.66% |
| iter 1900: loss 3.1529, time 2081.50ms, mfu 33.67% |
| iter 1910: loss 3.0617, time 2108.79ms, mfu 33.63% |
| iter 1920: loss 2.8841, time 2093.34ms, mfu 33.63% |
| iter 1930: loss 2.9809, time 2109.44ms, mfu 33.60% |
| iter 1940: loss 3.0346, time 2087.30ms, mfu 33.60% |
| iter 1950: loss 3.2623, time 2075.74ms, mfu 33.63% |
| iter 1960: loss 3.0727, time 2084.41ms, mfu 33.63% |
| iter 1970: loss 3.2350, time 2091.24ms, mfu 33.63% |
| iter 1980: loss 2.7521, time 2094.86ms, mfu 33.62% |
| iter 1990: loss 3.0140, time 2087.20ms, mfu 33.63% |
| step 2000: train loss 3.0943, val loss 3.1266 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 2000: loss 3.0097, time 9108.18ms, mfu 31.03% |
| iter 2010: loss 2.9544, time 2086.93ms, mfu 31.30% |
| iter 2020: loss 3.1589, time 2078.56ms, mfu 31.55% |
| iter 2030: loss 3.1263, time 2097.99ms, mfu 31.74% |
| iter 2040: loss 2.9587, time 2099.60ms, mfu 31.91% |
| iter 2050: loss 3.0223, time 2097.16ms, mfu 32.07% |
| iter 2060: loss 2.9487, time 2088.10ms, mfu 32.23% |
| iter 2070: loss 2.9620, time 2082.66ms, mfu 32.38% |
| iter 2080: loss 3.0641, time 2087.66ms, mfu 32.51% |
| iter 2090: loss 2.7195, time 2074.76ms, mfu 32.64% |
| iter 2100: loss 3.2061, time 2072.21ms, mfu 32.77% |
| iter 2110: loss 2.8790, time 2098.70ms, mfu 32.84% |
| iter 2120: loss 3.2879, time 2075.23ms, mfu 32.94% |
| iter 2130: loss 3.1826, time 2090.97ms, mfu 33.01% |
| iter 2140: loss 3.4425, time 2082.97ms, mfu 33.08% |
| iter 2150: loss 2.9741, time 2072.72ms, mfu 33.16% |
| iter 2160: loss 3.2976, time 2078.42ms, mfu 33.23% |
| iter 2170: loss 2.9592, time 2085.85ms, mfu 33.27% |
| iter 2180: loss 3.4325, time 2083.51ms, mfu 33.32% |
| iter 2190: loss 3.1819, time 2098.03ms, mfu 33.33% |
| iter 2200: loss 3.1337, time 2071.30ms, mfu 33.39% |
| iter 2210: loss 3.0284, time 2103.11ms, mfu 33.39% |
| iter 2220: loss 2.9793, time 2081.02ms, mfu 33.43% |
| iter 2230: loss 3.0734, time 2094.47ms, mfu 33.44% |
| iter 2240: loss 3.1064, time 2074.89ms, mfu 33.48% |
| iter 2250: loss 3.2566, time 2083.70ms, mfu 33.51% |
| iter 2260: loss 2.8806, time 2086.37ms, mfu 33.52% |
| iter 2270: loss 3.0763, time 2091.94ms, mfu 33.53% |
| iter 2280: loss 3.0993, time 2082.17ms, mfu 33.55% |
| iter 2290: loss 2.7687, time 2103.77ms, mfu 33.54% |
| iter 2300: loss 3.1544, time 2086.01ms, mfu 33.55% |
| iter 2310: loss 3.0680, time 2081.71ms, mfu 33.57% |
| iter 2320: loss 2.9684, time 2097.08ms, mfu 33.56% |
| iter 2330: loss 2.7440, time 2096.52ms, mfu 33.56% |
| iter 2340: loss 3.0904, time 2076.54ms, mfu 33.59% |
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| iter 2990: loss 3.0060, time 2109.60ms, mfu 33.68% |
| step 3000: train loss 2.9281, val loss 2.9812 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 3000: loss 3.0601, time 9151.48ms, mfu 31.08% |
| iter 3010: loss 2.7150, time 2084.09ms, mfu 31.34% |
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| iter 3990: loss 3.2189, time 2082.19ms, mfu 33.87% |
| step 4000: train loss 2.8648, val loss 2.9117 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 4000: loss 2.8254, time 9065.40ms, mfu 31.26% |
| iter 4010: loss 2.9850, time 2068.52ms, mfu 31.53% |
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| iter 4990: loss 3.0120, time 2066.03ms, mfu 34.03% |
| step 5000: train loss 2.8030, val loss 2.8569 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 5000: loss 2.8127, time 9037.83ms, mfu 31.40% |
| iter 5010: loss 2.7815, time 2060.24ms, mfu 31.67% |
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| iter 5990: loss 2.7603, time 2075.80ms, mfu 34.02% |
| step 6000: train loss 2.7348, val loss 2.7800 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 6000: loss 2.7937, time 9040.42ms, mfu 31.39% |
| iter 6010: loss 2.8561, time 2051.49ms, mfu 31.68% |
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| iter 6990: loss 2.8031, time 2066.51ms, mfu 33.97% |
| step 7000: train loss 2.7199, val loss 2.7451 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 7000: loss 2.6378, time 8990.41ms, mfu 31.35% |
| iter 7010: loss 2.3748, time 2058.15ms, mfu 31.63% |
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| iter 7990: loss 2.6368, time 2064.54ms, mfu 33.98% |
| step 8000: train loss 2.6810, val loss 2.7095 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 8000: loss 2.4861, time 12696.51ms, mfu 31.14% |
| iter 8010: loss 2.7566, time 2065.55ms, mfu 31.43% |
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| iter 8990: loss 2.9678, time 2058.52ms, mfu 34.04% |
| step 9000: train loss 2.6725, val loss 2.7046 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 9000: loss 2.6708, time 9067.21ms, mfu 31.41% |
| iter 9010: loss 2.3761, time 2059.76ms, mfu 31.68% |
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| iter 9990: loss 2.9309, time 2059.60ms, mfu 34.07% |
| step 10000: train loss 2.6098, val loss 2.6928 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 10000: loss 2.6818, time 9066.47ms, mfu 31.44% |
| iter 10010: loss 2.4153, time 2060.50ms, mfu 31.70% |
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| iter 10990: loss 2.6687, time 2057.86ms, mfu 34.03% |
| step 11000: train loss 2.6080, val loss 2.6397 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 11000: loss 2.4670, time 9053.47ms, mfu 31.40% |
| iter 11010: loss 2.6667, time 2060.61ms, mfu 31.67% |
| iter 11020: loss 2.3796, time 2061.45ms, mfu 31.91% |
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| iter 11990: loss 2.4477, time 2059.48ms, mfu 34.07% |
| step 12000: train loss 2.5605, val loss 2.6301 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 12000: loss 2.0776, time 9091.03ms, mfu 31.43% |
| iter 12010: loss 2.3315, time 2054.17ms, mfu 31.71% |
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| iter 12990: loss 2.4000, time 2066.20ms, mfu 34.07% |
| step 13000: train loss 2.5864, val loss 2.6138 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 13000: loss 2.7380, time 9066.45ms, mfu 31.44% |
| iter 13010: loss 2.3439, time 2049.57ms, mfu 31.72% |
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| iter 13990: loss 2.6802, time 2054.23ms, mfu 34.14% |
| step 14000: train loss 2.5575, val loss 2.6164 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 14000: loss 2.8029, time 9061.69ms, mfu 31.50% |
| iter 14010: loss 2.2803, time 2058.57ms, mfu 31.77% |
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| iter 14990: loss 2.5137, time 2062.48ms, mfu 34.22% |
| step 15000: train loss 2.5469, val loss 2.6056 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 15000: loss 2.5324, time 9050.78ms, mfu 31.57% |
| iter 15010: loss 2.3895, time 2047.69ms, mfu 31.85% |
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| iter 15990: loss 2.6321, time 2044.96ms, mfu 34.24% |
| step 16000: train loss 2.5108, val loss 2.5968 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 16000: loss 2.4993, time 9021.45ms, mfu 31.59% |
| iter 16010: loss 2.8461, time 2053.27ms, mfu 31.85% |
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| iter 16990: loss 2.4610, time 2046.12ms, mfu 34.30% |
| step 17000: train loss 2.5031, val loss 2.6176 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 17000: loss 2.5025, time 9049.82ms, mfu 31.64% |
| iter 17010: loss 2.4492, time 2046.85ms, mfu 31.91% |
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| iter 17990: loss 2.4377, time 2049.14ms, mfu 34.31% |
| step 18000: train loss 2.5104, val loss 2.5790 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 18000: loss 2.6239, time 9213.51ms, mfu 31.64% |
| iter 18010: loss 2.5675, time 2042.40ms, mfu 31.92% |
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| iter 18990: loss 2.8133, time 2073.84ms, mfu 34.29% |
| step 19000: train loss 2.4960, val loss 2.5795 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 19000: loss 2.6946, time 9157.05ms, mfu 31.63% |
| iter 19010: loss 2.6494, time 2067.11ms, mfu 31.87% |
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| iter 19990: loss 2.5672, time 2111.66ms, mfu 33.54% |
| step 20000: train loss 2.4705, val loss 2.5600 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 20000: loss 2.3049, time 9127.17ms, mfu 30.96% |
| iter 20010: loss 2.5311, time 2082.86ms, mfu 31.23% |
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| iter 20110: loss 2.6586, time 2126.40ms, mfu 32.71% |
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| iter 20990: loss 2.5255, time 2081.84ms, mfu 33.60% |
| step 21000: train loss 2.4877, val loss 2.5653 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 21000: loss 2.3033, time 9065.20ms, mfu 31.02% |
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| iter 21990: loss 2.8476, time 2034.09ms, mfu 34.33% |
| step 22000: train loss 2.4545, val loss 2.5775 |
| saving checkpoint to ../runs/minimal-en-125m-4ep |
| iter 22000: loss 2.6384, time 9025.38ms, mfu 31.68% |
| saving final checkpoint to ../runs/minimal-en-125m-4ep |
| [1;34mwandb[0m: |
| [1;34mwandb[0m: 🚀 View run [33m125m-12k-4ep[0m at: [34mhttps://wandb.ai/dawidmajewski-ezotic/minimal-en/runs/hlbpqpoe[0m |
| [1;34mwandb[0m: Find logs at: [1;35m../logs/wandb/wandb/run-20260824_231331-hlbpqpoe/logs[0m |
| |
