| 🎯 Starting continuous training for 12 hours... | |
| 🤖 Autonomous evolution mode activated | |
| 🚀 Setting up training environment... | |
| 📊 GPU: NVIDIA H200 NVL | |
| 💾 GPU Memory: 139.8 GB | |
| 🤖 Autonomous evolution mode: ENABLED | |
| 📦 Loading model and tokenizer... | |
| Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. | |
| Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s] Loading checkpoint shards: 25%|██▌ | 1/4 [00:00<00:02, 1.21it/s] Loading checkpoint shards: 50%|█████ | 2/4 [00:01<00:01, 1.23it/s] Loading checkpoint shards: 75%|███████▌ | 3/4 [00:02<00:00, 1.26it/s] Loading checkpoint shards: 100%|██████████| 4/4 [00:03<00:00, 1.29it/s] Loading checkpoint shards: 100%|██████████| 4/4 [00:03<00:00, 1.27it/s] | |
| ✅ Model loaded: qwen2 | |
| ✅ Tokenizer vocab size: 151665 | |
| 📥 Loading Elizabeth corpus data... | |
| ✅ Loaded 3000 high-quality security-focused conversations | |
| ✅ Formatted 3000 training texts | |
| Map: 0%| | 0/3000 [00:00<?, ? examples/s] Map: 67%|██████▋ | 2000/3000 [00:00<00:00, 16267.08 examples/s] Map: 100%|██████████| 3000/3000 [00:00<00:00, 13459.72 examples/s] | |
| ✅ Tokenized dataset: 3000 examples | |
| ⚙️ Setting up training... | |
| 🔥 Starting training... | |
| 📈 Batch size: 4 | |
| 📈 Gradient accumulation: 16 | |
| 📈 Effective batch size: 64 | |
| ⏰ Continuous training mode: 12 hours autonomous evolution | |
| 0%| | 0/92 [00:00<?, ?it/s]/home/x/.local/lib/python3.12/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. | |
| warnings.warn( | |
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| 43%|████▎ | 40/92 [01:38<02:14, 2.59s/it] 45%|████▍ | 41/92 [01:41<02:13, 2.63s/it] 46%|████▌ | 42/92 [01:43<02:09, 2.60s/it] 47%|████▋ | 43/92 [01:47<02:20, 2.88s/it] 48%|████▊ | 44/92 [01:49<02:12, 2.76s/it] 49%|████▉ | 45/92 [01:52<02:09, 2.75s/it] 50%|█████ | 46/92 [01:55<02:13, 2.90s/it] 51%|█████ | 47/92 [01:58<02:04, 2.77s/it] 52%|█████▏ | 48/92 [02:01<02:01, 2.76s/it] 53%|█████▎ | 49/92 [02:03<01:52, 2.61s/it] 54%|█████▍ | 50/92 [02:05<01:42, 2.44s/it] {'loss': 0.0266, 'grad_norm': 0.8984375, 'learning_rate': 1.0383027336900356e-05, 'epoch': 1.07} | |
| 54%|█████▍ | 50/92 [02:05<01:42, 2.44s/it] 55%|█████▌ | 51/92 [02:07<01:36, 2.36s/it] 57%|█████▋ | 52/92 [02:10<01:39, 2.48s/it] 58%|█████▊ | 53/92 [02:13<01:39, 2.56s/it] 59%|█████▊ | 54/92 [02:16<01:42, 2.70s/it] 60%|█████▉ | 55/92 [02:18<01:33, 2.52s/it] 61%|██████ | 56/92 [02:20<01:26, 2.40s/it] 62%|██████▏ | 57/92 [02:23<01:31, 2.62s/it] 63%|██████▎ | 58/92 [02:25<01:24, 2.48s/it] 64%|██████▍ | 59/92 [02:28<01:21, 2.46s/it] 65%|██████▌ | 60/92 [02:30<01:22, 2.58s/it] {'loss': 0.0228, 'grad_norm': 0.54296875, 'learning_rate': 6.619831215914974e-06, 'epoch': 1.28} | |
| 65%|██████▌ | 60/92 [02:30<01:22, 2.58s/it] 66%|██████▋ | 61/92 [02:33<01:18, 2.54s/it] 67%|██████▋ | 62/92 [02:36<01:18, 2.60s/it] 68%|██████▊ | 63/92 [02:38<01:14, 2.55s/it] 70%|██████▉ | 64/92 [02:40<01:08, 2.43s/it] 71%|███████ | 65/92 [02:42<01:03, 2.37s/it] 72%|███████▏ | 66/92 [02:44<00:57, 2.21s/it] 73%|███████▎ | 67/92 [02:47<00:57, 2.30s/it] 74%|███████▍ | 68/92 [02:49<00:58, 2.43s/it] 75%|███████▌ | 69/92 [02:52<00:56, 2.45s/it] 76%|███████▌ | 70/92 [02:55<00:56, 2.56s/it] {'loss': 0.022, 'grad_norm': 0.474609375, 'learning_rate': 3.3467429983443477e-06, 'epoch': 1.49} | |
| 76%|███████▌ | 70/92 [02:55<00:56, 2.56s/it] 77%|███████▋ | 71/92 [02:57<00:54, 2.60s/it] 78%|███████▊ | 72/92 [03:00<00:48, 2.44s/it] 79%|███████▉ | 73/92 [03:02<00:47, 2.52s/it] 80%|████████ | 74/92 [03:05<00:45, 2.53s/it] 82%|████████▏ | 75/92 [03:07<00:41, 2.42s/it] 83%|████████▎ | 76/92 [03:09<00:38, 2.38s/it] 84%|████████▎ | 77/92 [03:12<00:37, 2.53s/it] 85%|████████▍ | 78/92 [03:15<00:36, 2.60s/it] 86%|████████▌ | 79/92 [03:18<00:35, 2.77s/it] 87%|████████▋ | 80/92 [03:20<00:29, 2.46s/it] {'loss': 0.0211, 'grad_norm': 0.55859375, 'learning_rate': 1.0383444303894453e-06, 'epoch': 1.71} | |
| 87%|████████▋ | 80/92 [03:20<00:29, 2.46s/it] 88%|████████▊ | 81/92 [03:22<00:25, 2.35s/it] 89%|████████▉ | 82/92 [03:25<00:25, 2.57s/it] 90%|█████████ | 83/92 [03:28<00:23, 2.63s/it] 91%|█████████▏| 84/92 [03:31<00:21, 2.72s/it] 92%|█████████▏| 85/92 [03:33<00:17, 2.46s/it] 93%|█████████▎| 86/92 [03:35<00:14, 2.40s/it] 95%|█████████▍| 87/92 [03:38<00:13, 2.64s/it] 96%|█████████▌| 88/92 [03:40<00:09, 2.40s/it] 97%|█████████▋| 89/92 [03:42<00:07, 2.39s/it] 98%|█████████▊| 90/92 [03:44<00:04, 2.33s/it] {'loss': 0.0215, 'grad_norm': 0.625, 'learning_rate': 2.9341988162595593e-08, 'epoch': 1.92} | |
| 98%|█████████▊| 90/92 [03:44<00:04, 2.33s/it] 99%|█████████▉| 91/92 [03:47<00:02, 2.41s/it] 100%|██████████| 92/92 [03:50<00:00, 2.54s/it] {'train_runtime': 230.3452, 'train_samples_per_second': 26.048, 'train_steps_per_second': 0.399, 'train_loss': 0.3904266621836502, 'epoch': 1.96} | |
| 100%|██████████| 92/92 [03:50<00:00, 2.54s/it] 100%|██████████| 92/92 [03:50<00:00, 2.50s/it] | |
| ✅ Training completed in 0.07 hours | |
| 🎉 Training pipeline completed successfully! | |