Trained classifier model on MIMIC-IV
Browse files
classification_log_2025-06-13_18-45-53.log
ADDED
|
@@ -0,0 +1,663 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
2025-06-13 18:45:53,711 - INFO - ================================================================================ - [multilabel_classify.py:101:log_section]
|
| 2 |
+
2025-06-13 18:45:53,711 - INFO - = 📌 INITIALIZING TRAINING ENVIRONMENT = - [multilabel_classify.py:102:log_section]
|
| 3 |
+
2025-06-13 18:45:53,711 - INFO - ================================================================================ - [multilabel_classify.py:105:log_section]
|
| 4 |
+
2025-06-13 18:45:53,711 - INFO - 🚀 Setting up data paths and environment variables... - [multilabel_classify.py:3916:main]
|
| 5 |
+
2025-06-13 18:45:53,712 - INFO - 📂 Using output directory: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:3922:main]
|
| 6 |
+
2025-06-13 18:45:53,712 - INFO - 🛠️ Command-line Arguments: - [multilabel_classify.py:369:print_args]
|
| 7 |
+
2025-06-13 18:45:53,712 - INFO -
|
| 8 |
+
🔹 output_dir: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b
|
| 9 |
+
🔹 source_url: XURLs.MIMIC4_DEMO
|
| 10 |
+
🔹 data: mimic4_icd10_full
|
| 11 |
+
🔹 logfile: classification_log
|
| 12 |
+
🔹 base_dir: ../tmp/MIMIC4_DEMO
|
| 13 |
+
🔹 hub_model_id: deb101/mistral-7b-instruct-v0.3-mimic4-adapt
|
| 14 |
+
🔹 model_name: mistralai/Mistral-7B-Instruct-v0.3
|
| 15 |
+
🔹 max_length: 512
|
| 16 |
+
🔹 do_fresh_training: True
|
| 17 |
+
🔹 load_from_checkpoint: False
|
| 18 |
+
🔹 task: multilabel-classify
|
| 19 |
+
🔹 num_train_epochs: 1
|
| 20 |
+
🔹 per_device_train_batch_size: 8
|
| 21 |
+
🔹 per_device_eval_batch_size: 8
|
| 22 |
+
🔹 metric_for_best_model: precision_at_15
|
| 23 |
+
🔹 learning_rate: 0.0001
|
| 24 |
+
🔹 final_lr_scheduling: 1e-06
|
| 25 |
+
🔹 warmup_steps: 500
|
| 26 |
+
🔹 logfile_path: ../tmp/logs/classification_log_2025-06-13_18-45-53.log
|
| 27 |
+
🔹 source: /home/ubuntu/.xcube/data/mimic4_demo - [multilabel_classify.py:370:print_args]
|
| 28 |
+
2025-06-13 18:45:53,712 - INFO - ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ - [multilabel_classify.py:371:print_args]
|
| 29 |
+
2025-06-13 18:45:53,722 - INFO -
|
| 30 |
+
🚀 Quick Git Info: 📁 xcube | 🌿 plant | 🔍 0bd4309 | 👤 Debjyoti Saha Roy | ⚡ MIXED (1 staged, 2 unstaged) | 🔬 git show 0bd4309 - [multilabel_classify.py:3928:main]
|
| 31 |
+
2025-06-13 18:45:53,722 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 32 |
+
2025-06-13 18:45:53,723 - INFO - + ✨ LOADING DATASETS + - [multilabel_classify.py:102:log_section]
|
| 33 |
+
2025-06-13 18:45:53,723 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 34 |
+
2025-06-13 18:45:53,723 - INFO - 📊 Loading main datasets.... - [multilabel_classify.py:3931:main]
|
| 35 |
+
2025-06-13 18:46:02,259 - INFO - 🔍 Total unique labels in dataset: 7942 - [multilabel_classify.py:3707:sample_df_with_full_label_coverage]
|
| 36 |
+
2025-06-13 18:46:02,272 - INFO - 🧪 Attempt 1: Sampled 122 rows covering 863 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 37 |
+
2025-06-13 18:46:02,282 - INFO - 🧪 Attempt 2: Sampled 122 rows covering 816 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 38 |
+
2025-06-13 18:46:02,291 - INFO - 🧪 Attempt 3: Sampled 122 rows covering 885 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 39 |
+
2025-06-13 18:46:02,300 - INFO - 🧪 Attempt 4: Sampled 122 rows covering 828 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 40 |
+
2025-06-13 18:46:02,309 - INFO - 🧪 Attempt 5: Sampled 122 rows covering 879 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 41 |
+
2025-06-13 18:46:02,317 - INFO - 🧪 Attempt 6: Sampled 122 rows covering 852 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 42 |
+
2025-06-13 18:46:02,326 - INFO - 🧪 Attempt 7: Sampled 122 rows covering 838 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 43 |
+
2025-06-13 18:46:02,335 - INFO - 🧪 Attempt 8: Sampled 122 rows covering 851 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 44 |
+
2025-06-13 18:46:02,343 - INFO - 🧪 Attempt 9: Sampled 122 rows covering 825 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 45 |
+
2025-06-13 18:46:02,351 - INFO - 🧪 Attempt 10: Sampled 122 rows covering 833 labels. - [multilabel_classify.py:3721:sample_df_with_full_label_coverage]
|
| 46 |
+
2025-06-13 18:46:02,356 - INFO - 🛠️ Fixing missing labels: 7109 remaining... - [multilabel_classify.py:3754:sample_df_with_full_label_coverage]
|
| 47 |
+
2025-06-13 18:49:30,886 - INFO - ✅ Added 1648 rows to achieve full label coverage. - [multilabel_classify.py:3786:sample_df_with_full_label_coverage]
|
| 48 |
+
2025-06-13 18:49:30,889 - INFO - 📊 Final total labels: 7942 - [multilabel_classify.py:3789:sample_df_with_full_label_coverage]
|
| 49 |
+
2025-06-13 18:49:30,889 - INFO - ✅ Final row count: 1770 (Valid: 420, Not-valid: 1350) - [multilabel_classify.py:3797:sample_df_with_full_label_coverage]
|
| 50 |
+
2025-06-13 18:49:31,659 - INFO - ******************************************************************************** - [multilabel_classify.py:101:log_section]
|
| 51 |
+
2025-06-13 18:49:31,659 - INFO - * 🌟 STARTING MULTI_LABEL CLASSIFICATION MODEL TRAINING * - [multilabel_classify.py:102:log_section]
|
| 52 |
+
2025-06-13 18:49:31,659 - INFO - ******************************************************************************** - [multilabel_classify.py:105:log_section]
|
| 53 |
+
2025-06-13 18:49:31,659 - INFO - 🔐 Loaded authentication token from environment - [multilabel_classify.py:3958:main]
|
| 54 |
+
2025-06-13 18:49:31,659 - INFO - 🏷️ Hub Model ID for this Classification task: deb101/mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify - [multilabel_classify.py:3962:main]
|
| 55 |
+
2025-06-13 18:49:31,659 - INFO - -------------------------------------------------------------------------------- - [multilabel_classify.py:101:log_section]
|
| 56 |
+
2025-06-13 18:49:31,659 - INFO - - 📋 MODEL EXISTENCE CHECK - - [multilabel_classify.py:102:log_section]
|
| 57 |
+
2025-06-13 18:49:31,659 - INFO - -------------------------------------------------------------------------------- - [multilabel_classify.py:105:log_section]
|
| 58 |
+
2025-06-13 18:49:31,659 - INFO - 🔍 Checking model existence locally and on Hugging Face Hub... - [multilabel_classify.py:3822:check_model_existence]
|
| 59 |
+
2025-06-13 18:49:31,659 - INFO - ❌ Model not found locally at: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:3829:check_model_existence]
|
| 60 |
+
2025-06-13 18:49:31,726 - INFO - ✅ Model exists on Hugging Face Hub with ID: deb101/mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify - [multilabel_classify.py:3841:check_model_existence]
|
| 61 |
+
2025-06-13 18:49:31,726 - INFO - 📁 Model exists either locally or on Hub - [multilabel_classify.py:3867:check_model_existence]
|
| 62 |
+
2025-06-13 18:49:31,726 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 63 |
+
2025-06-13 18:49:31,726 - INFO - + ✨ STARTING FRESH TRAINING + - [multilabel_classify.py:102:log_section]
|
| 64 |
+
2025-06-13 18:49:31,727 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 65 |
+
2025-06-13 18:49:31,727 - INFO - 🔄 Starting fresh training (either forced or model not found)... - [multilabel_classify.py:3975:main]
|
| 66 |
+
2025-06-13 18:49:31,738 - WARNING - Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured. - [_login.py:415:_login]
|
| 67 |
+
2025-06-13 18:49:31,738 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 68 |
+
2025-06-13 18:49:31,738 - INFO - + ✨ LOADING BASE MODEL + - [multilabel_classify.py:102:log_section]
|
| 69 |
+
2025-06-13 18:49:31,738 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 70 |
+
2025-06-13 18:49:31,738 - INFO - 📥 Loading pretrained model and tokenizer... - [multilabel_classify.py:4007:main]
|
| 71 |
+
2025-06-13 18:49:31,738 - INFO - 🚀 Starting model and tokenizer loading process... - [multilabel_classify.py:1579:load_base_model_and_tokenizer]
|
| 72 |
+
2025-06-13 18:49:31,739 - INFO - 📊 Quantization config: 4-bit, nf4, double_quant, bfloat16 - [multilabel_classify.py:1588:load_base_model_and_tokenizer]
|
| 73 |
+
2025-06-13 18:49:31,739 - INFO - 🔤 Loading tokenizer for model: deb101/mistral-7b-instruct-v0.3-mimic4-adapt... - [multilabel_classify.py:1592:load_base_model_and_tokenizer]
|
| 74 |
+
2025-06-13 18:49:32,680 - INFO - 🔍 Checking if deb101/mistral-7b-instruct-v0.3-mimic4-adapt is a PEFT model... - [multilabel_classify.py:1603:load_base_model_and_tokenizer]
|
| 75 |
+
2025-06-13 18:49:32,735 - INFO - ✅ Detected PEFT model. Base model: mistralai/Mistral-7B-Instruct-v0.3 - [multilabel_classify.py:1607:load_base_model_and_tokenizer]
|
| 76 |
+
2025-06-13 18:49:32,735 - INFO - 🔍 Loading model configuration for mistralai/Mistral-7B-Instruct-v0.3... - [multilabel_classify.py:1615:load_base_model_and_tokenizer]
|
| 77 |
+
2025-06-13 18:49:32,810 - INFO - Model type: mistral, Architectures: ['MistralForCausalLM'] - [multilabel_classify.py:1630:load_base_model_and_tokenizer]
|
| 78 |
+
2025-06-13 18:49:32,810 - INFO - 🧠 Loading base model: mistralai/Mistral-7B-Instruct-v0.3... - [multilabel_classify.py:1698:load_base_model_and_tokenizer]
|
| 79 |
+
2025-06-13 18:49:33,322 - INFO - We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk). - [modeling.py:991:get_balanced_memory]
|
| 80 |
+
2025-06-13 18:49:38,581 - INFO - 🧩 Loading PEFT adapters for deb101/mistral-7b-instruct-v0.3-mimic4-adapt... - [multilabel_classify.py:1718:load_base_model_and_tokenizer]
|
| 81 |
+
2025-06-13 18:49:39,365 - INFO - 🔧 Before enabling PEFT adapters - [multilabel_classify.py:1720:load_base_model_and_tokenizer]
|
| 82 |
+
2025-06-13 18:49:39,367 - INFO - 📊 trainable params: 0 || all params: 7,254,839,296 || trainable%: 0.0000 - [multilabel_classify.py:160:log_print_output]
|
| 83 |
+
2025-06-13 18:49:39,370 - INFO - Enabled gradients for parameters: ['base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.0.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.1.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.1.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.1.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.1.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.2.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.2.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.2.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.2.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.3.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.3.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.3.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.3.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.4.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.4.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.4.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.4.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.5.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.5.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.5.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.5.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.6.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.6.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.6.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.6.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.7.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.7.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.7.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.7.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.8.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.8.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.8.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.8.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.9.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.9.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.9.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.9.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.10.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.10.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.10.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.10.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.11.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.11.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.11.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.11.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.12.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.12.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.12.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.12.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.13.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.13.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.13.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.13.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.14.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.14.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.14.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.14.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.15.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.15.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.15.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.15.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.16.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.16.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.16.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.16.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.17.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.17.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.17.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.17.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.18.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.18.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.18.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.18.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.19.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.19.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.19.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.19.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.20.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.20.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.20.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.20.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.21.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.21.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.21.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.21.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.22.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.22.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.22.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.22.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.23.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.23.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.23.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.23.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.24.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.24.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.24.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.24.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.25.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.25.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.25.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.25.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.26.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.26.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.26.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.26.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.27.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.27.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.27.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.27.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.28.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.28.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.28.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.28.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.29.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.29.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.29.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.29.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.30.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.30.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.30.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.30.self_attn.v_proj.lora_B.default.weight', 'base_model.model.model.layers.31.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.layers.31.self_attn.q_proj.lora_B.default.weight', 'base_model.model.model.layers.31.self_attn.v_proj.lora_A.default.weight', 'base_model.model.model.layers.31.self_attn.v_proj.lora_B.default.weight'] - [multilabel_classify.py:1730:load_base_model_and_tokenizer]
|
| 84 |
+
2025-06-13 18:49:39,370 - INFO - 🔧 After enabling PEFT adapters - [multilabel_classify.py:1731:load_base_model_and_tokenizer]
|
| 85 |
+
2025-06-13 18:49:39,372 - INFO - 📊 trainable params: 6,815,744 || all params: 7,254,839,296 || trainable%: 0.0939 - [multilabel_classify.py:160:log_print_output]
|
| 86 |
+
2025-06-13 18:49:39,374 - INFO - ✅ Model and tokenizer successfully loaded! - [multilabel_classify.py:1769:load_base_model_and_tokenizer]
|
| 87 |
+
2025-06-13 18:49:39,374 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 88 |
+
2025-06-13 18:49:39,374 - INFO - + ✨ DATA PREPROCESSING + - [multilabel_classify.py:102:log_section]
|
| 89 |
+
2025-06-13 18:49:39,374 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 90 |
+
2025-06-13 18:49:39,374 - INFO - 🔄 Loading and preprocessing training data... - [multilabel_classify.py:4017:main]
|
| 91 |
+
2025-06-13 18:49:39,553 - INFO - Total number of labels: 7942 - [multilabel_classify.py:1172:preprocess_data]
|
| 92 |
+
2025-06-13 18:49:39,553 - INFO - Rare labels (freq < 50): 7817 - [multilabel_classify.py:1173:preprocess_data]
|
| 93 |
+
2025-06-13 18:49:39,553 - INFO - Not rare labels (freq >= 50): 125 - [multilabel_classify.py:1174:preprocess_data]
|
| 94 |
+
2025-06-13 18:49:39,553 - INFO - Label partitions and classes saved to ../tmp/MIMIC4_DEMO/labels_partition.json - [multilabel_classify.py:1175:preprocess_data]
|
| 95 |
+
2025-06-13 18:50:36,704 - INFO - The size of training set: 8393 - [multilabel_classify.py:1271:preprocess_data]
|
| 96 |
+
2025-06-13 18:50:36,704 - INFO - The size of Evaluation set: 2528 - [multilabel_classify.py:1272:preprocess_data]
|
| 97 |
+
2025-06-13 18:50:37,110 - INFO - Number of unique ICD-10 codes: 7942 - [multilabel_classify.py:4023:main]
|
| 98 |
+
2025-06-13 18:50:37,112 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 99 |
+
2025-06-13 18:50:37,112 - INFO - + ✨ MODEL INITIALIZATION + - [multilabel_classify.py:102:log_section]
|
| 100 |
+
2025-06-13 18:50:37,112 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 101 |
+
2025-06-13 18:50:37,112 - INFO - 🧠 Initializing custom L2R model for outputting per-token relevance scores per ICD-10 codes. - [multilabel_classify.py:4026:main]
|
| 102 |
+
2025-06-13 18:50:37,113 - INFO - 🏥📊 Creating MultilabelICDClassifier - Standard multilabel medical classifier! 🔬💫 - [multilabel_classify.py:860:define_model]
|
| 103 |
+
2025-06-13 18:50:37,113 - INFO - Will now start to create Multilabel-Classification Model from the base model - [multilabel_classify.py:565:__init__]
|
| 104 |
+
2025-06-13 18:50:37,117 - INFO - 📊 trainable params: 6,815,744 || all params: 3,765,178,368 || trainable%: 0.1810 - [multilabel_classify.py:619:compute_trainable_params]
|
| 105 |
+
2025-06-13 18:50:38,856 - INFO - Creating the Multi-Label Classification Model from base model mistralai/Mistral-7B-Instruct-v0.3 completed!!! - [multilabel_classify.py:607:__init__]
|
| 106 |
+
2025-06-13 18:50:38,860 - INFO - 📊 trainable params: 171,532,417 || all params: 3,929,895,041 || trainable%: 4.3648 - [multilabel_classify.py:619:compute_trainable_params]
|
| 107 |
+
2025-06-13 18:50:38,860 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 108 |
+
2025-06-13 18:50:38,860 - INFO - + ✨ TRAINING PREPARATION + - [multilabel_classify.py:102:log_section]
|
| 109 |
+
2025-06-13 18:50:38,860 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 110 |
+
2025-06-13 18:50:38,861 - INFO - ⚙️ Preparing training components and optimizers... - [multilabel_classify.py:4033:main]
|
| 111 |
+
2025-06-13 18:50:38,945 - INFO - 🖥️ Device: NVIDIA GH200 480GB - [multilabel_classify.py:1019:log_training_configuration]
|
| 112 |
+
2025-06-13 18:50:38,945 - INFO - 🔋 CUDA Available: True - [multilabel_classify.py:1022:log_training_configuration]
|
| 113 |
+
2025-06-13 18:50:38,945 - INFO - 💾 CUDA Device Count: 1 - [multilabel_classify.py:1023:log_training_configuration]
|
| 114 |
+
2025-06-13 18:50:38,947 - INFO -
|
| 115 |
+
📋 Training Configuration 📋
|
| 116 |
+
+----------+-----------------------------+------------------------------------------------------------------+
|
| 117 |
+
| 🌟 Emoji | 🏷️ Parameter | 📊 Value |
|
| 118 |
+
+----------+-----------------------------+------------------------------------------------------------------+
|
| 119 |
+
| 📁 | Output Directory | ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b |
|
| 120 |
+
| 🔁 | Training Epochs | 1 |
|
| 121 |
+
| 🏋️ | Train Batch Size | 8 |
|
| 122 |
+
| 🔍 | Eval Batch Size | 8 |
|
| 123 |
+
| 📊 | Gradient Accumulation Steps | 4 |
|
| 124 |
+
| 🚀 | Learning Rate | 0.0001 |
|
| 125 |
+
| 🌅 | Warmup Steps | 500 |
|
| 126 |
+
| 💾 | Save Strategy | epoch |
|
| 127 |
+
| 💾 | Save Total Limit | 10 |
|
| 128 |
+
| 📊 | Evaluation Strategy | epoch |
|
| 129 |
+
| 🎯 | Best Model Metric | precision_at_15 |
|
| 130 |
+
| 📝 | Logging Strategy | steps (every 10 steps) |
|
| 131 |
+
| 🌐 | Push to Hub | True |
|
| 132 |
+
| 🌐 | Hub Model ID | deb101/mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify |
|
| 133 |
+
| 🔢 | Steps per Epoch | 262 |
|
| 134 |
+
| 🔢 | Total Training Steps | 262 |
|
| 135 |
+
| 🔢 | Evaluation Steps | 316 |
|
| 136 |
+
| 📊 | Training Dataset Size | 8393 samples 🏋️ |
|
| 137 |
+
| 📊 | Evaluation Dataset Size | 2528 samples 🔍 |
|
| 138 |
+
+----------+-----------------------------+------------------------------------------------------------------+ - [multilabel_classify.py:1011:log_training_args]
|
| 139 |
+
2025-06-13 18:50:38,947 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 140 |
+
2025-06-13 18:50:38,948 - INFO - + ✨ MODEL TRAINING + - [multilabel_classify.py:102:log_section]
|
| 141 |
+
2025-06-13 18:50:38,948 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 142 |
+
2025-06-13 18:50:38,948 - INFO - 🏋️ Starting model training process... - [multilabel_classify.py:4055:main]
|
| 143 |
+
2025-06-13 18:50:38,998 - INFO - We are registering the tokenizer deb101/mistral-7b-instruct-v0.3-mimic4-adapt in Custom Trainer - [multilabel_classify.py:2340:__init__]
|
| 144 |
+
2025-06-13 18:50:39,246 - INFO - 🚀 Starting Training... - [multilabel_classify.py:1994:on_train_begin]
|
| 145 |
+
2025-06-13 18:51:01,764 - INFO -
|
| 146 |
+
[36m🚂 Training Metrics (Step 10) 🚂
|
| 147 |
+
+---------------+----------+
|
| 148 |
+
| Metric | Value |
|
| 149 |
+
+===============+==========+
|
| 150 |
+
| loss | 0.6752 |
|
| 151 |
+
+---------------+----------+
|
| 152 |
+
| grad_norm | 7.27432 |
|
| 153 |
+
+---------------+----------+
|
| 154 |
+
| learning_rate | 2e-06 |
|
| 155 |
+
+---------------+----------+
|
| 156 |
+
| epoch | 0.038095 |
|
| 157 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 158 |
+
2025-06-13 18:51:21,360 - INFO -
|
| 159 |
+
[36m🚂 Training Metrics (Step 20) 🚂
|
| 160 |
+
+---------------+---------+
|
| 161 |
+
| Metric | Value |
|
| 162 |
+
+===============+=========+
|
| 163 |
+
| loss | 0.3475 |
|
| 164 |
+
+---------------+---------+
|
| 165 |
+
| grad_norm | 2.94909 |
|
| 166 |
+
+---------------+---------+
|
| 167 |
+
| learning_rate | 4e-06 |
|
| 168 |
+
+---------------+---------+
|
| 169 |
+
| epoch | 0.07619 |
|
| 170 |
+
+---------------+---------+[0m - [multilabel_classify.py:2188:on_log]
|
| 171 |
+
2025-06-13 18:51:40,988 - INFO -
|
| 172 |
+
[36m🚂 Training Metrics (Step 30) 🚂
|
| 173 |
+
+---------------+----------+
|
| 174 |
+
| Metric | Value |
|
| 175 |
+
+===============+==========+
|
| 176 |
+
| loss | 0.0737 |
|
| 177 |
+
+---------------+----------+
|
| 178 |
+
| grad_norm | 0.276477 |
|
| 179 |
+
+---------------+----------+
|
| 180 |
+
| learning_rate | 6e-06 |
|
| 181 |
+
+---------------+----------+
|
| 182 |
+
| epoch | 0.114286 |
|
| 183 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 184 |
+
2025-06-13 18:52:00,566 - INFO -
|
| 185 |
+
[36m🚂 Training Metrics (Step 40) 🚂
|
| 186 |
+
+---------------+----------+
|
| 187 |
+
| Metric | Value |
|
| 188 |
+
+===============+==========+
|
| 189 |
+
| loss | 0.0233 |
|
| 190 |
+
+---------------+----------+
|
| 191 |
+
| grad_norm | 0.104187 |
|
| 192 |
+
+---------------+----------+
|
| 193 |
+
| learning_rate | 8e-06 |
|
| 194 |
+
+---------------+----------+
|
| 195 |
+
| epoch | 0.152381 |
|
| 196 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 197 |
+
2025-06-13 18:52:20,179 - INFO -
|
| 198 |
+
[36m🚂 Training Metrics (Step 50) 🚂
|
| 199 |
+
+---------------+----------+
|
| 200 |
+
| Metric | Value |
|
| 201 |
+
+===============+==========+
|
| 202 |
+
| loss | 0.0282 |
|
| 203 |
+
+---------------+----------+
|
| 204 |
+
| grad_norm | 0.154837 |
|
| 205 |
+
+---------------+----------+
|
| 206 |
+
| learning_rate | 1e-05 |
|
| 207 |
+
+---------------+----------+
|
| 208 |
+
| epoch | 0.190476 |
|
| 209 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 210 |
+
2025-06-13 18:52:39,823 - INFO -
|
| 211 |
+
[36m🚂 Training Metrics (Step 60) 🚂
|
| 212 |
+
+---------------+----------+
|
| 213 |
+
| Metric | Value |
|
| 214 |
+
+===============+==========+
|
| 215 |
+
| loss | 0.027 |
|
| 216 |
+
+---------------+----------+
|
| 217 |
+
| grad_norm | 0.136466 |
|
| 218 |
+
+---------------+----------+
|
| 219 |
+
| learning_rate | 1.2e-05 |
|
| 220 |
+
+---------------+----------+
|
| 221 |
+
| epoch | 0.228571 |
|
| 222 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 223 |
+
2025-06-13 18:52:59,512 - INFO -
|
| 224 |
+
[36m🚂 Training Metrics (Step 70) 🚂
|
| 225 |
+
+---------------+----------+
|
| 226 |
+
| Metric | Value |
|
| 227 |
+
+===============+==========+
|
| 228 |
+
| loss | 0.0244 |
|
| 229 |
+
+---------------+----------+
|
| 230 |
+
| grad_norm | 0.029749 |
|
| 231 |
+
+---------------+----------+
|
| 232 |
+
| learning_rate | 1.4e-05 |
|
| 233 |
+
+---------------+----------+
|
| 234 |
+
| epoch | 0.266667 |
|
| 235 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 236 |
+
2025-06-13 18:53:19,196 - INFO -
|
| 237 |
+
[36m🚂 Training Metrics (Step 80) 🚂
|
| 238 |
+
+---------------+----------+
|
| 239 |
+
| Metric | Value |
|
| 240 |
+
+===============+==========+
|
| 241 |
+
| loss | 0.0234 |
|
| 242 |
+
+---------------+----------+
|
| 243 |
+
| grad_norm | 0.042736 |
|
| 244 |
+
+---------------+----------+
|
| 245 |
+
| learning_rate | 1.6e-05 |
|
| 246 |
+
+---------------+----------+
|
| 247 |
+
| epoch | 0.304762 |
|
| 248 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 249 |
+
2025-06-13 18:53:38,910 - INFO -
|
| 250 |
+
[36m🚂 Training Metrics (Step 90) 🚂
|
| 251 |
+
+---------------+----------+
|
| 252 |
+
| Metric | Value |
|
| 253 |
+
+===============+==========+
|
| 254 |
+
| loss | 0.0235 |
|
| 255 |
+
+---------------+----------+
|
| 256 |
+
| grad_norm | 0.035706 |
|
| 257 |
+
+---------------+----------+
|
| 258 |
+
| learning_rate | 1.8e-05 |
|
| 259 |
+
+---------------+----------+
|
| 260 |
+
| epoch | 0.342857 |
|
| 261 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 262 |
+
2025-06-13 18:53:58,615 - INFO -
|
| 263 |
+
[36m🚂 Training Metrics (Step 100) 🚂
|
| 264 |
+
+---------------+----------+
|
| 265 |
+
| Metric | Value |
|
| 266 |
+
+===============+==========+
|
| 267 |
+
| loss | 0.0243 |
|
| 268 |
+
+---------------+----------+
|
| 269 |
+
| grad_norm | 0.230328 |
|
| 270 |
+
+---------------+----------+
|
| 271 |
+
| learning_rate | 2e-05 |
|
| 272 |
+
+---------------+----------+
|
| 273 |
+
| epoch | 0.380952 |
|
| 274 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 275 |
+
2025-06-13 18:54:18,318 - INFO -
|
| 276 |
+
[36m🚂 Training Metrics (Step 110) 🚂
|
| 277 |
+
+---------------+----------+
|
| 278 |
+
| Metric | Value |
|
| 279 |
+
+===============+==========+
|
| 280 |
+
| loss | 0.023 |
|
| 281 |
+
+---------------+----------+
|
| 282 |
+
| grad_norm | 0.011574 |
|
| 283 |
+
+---------------+----------+
|
| 284 |
+
| learning_rate | 2.2e-05 |
|
| 285 |
+
+---------------+----------+
|
| 286 |
+
| epoch | 0.419048 |
|
| 287 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 288 |
+
2025-06-13 18:54:38,052 - INFO -
|
| 289 |
+
[36m🚂 Training Metrics (Step 120) 🚂
|
| 290 |
+
+---------------+----------+
|
| 291 |
+
| Metric | Value |
|
| 292 |
+
+===============+==========+
|
| 293 |
+
| loss | 0.0223 |
|
| 294 |
+
+---------------+----------+
|
| 295 |
+
| grad_norm | 0.01187 |
|
| 296 |
+
+---------------+----------+
|
| 297 |
+
| learning_rate | 2.4e-05 |
|
| 298 |
+
+---------------+----------+
|
| 299 |
+
| epoch | 0.457143 |
|
| 300 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 301 |
+
2025-06-13 18:54:57,782 - INFO -
|
| 302 |
+
[36m🚂 Training Metrics (Step 130) 🚂
|
| 303 |
+
+---------------+----------+
|
| 304 |
+
| Metric | Value |
|
| 305 |
+
+===============+==========+
|
| 306 |
+
| loss | 0.0234 |
|
| 307 |
+
+---------------+----------+
|
| 308 |
+
| grad_norm | 0.008039 |
|
| 309 |
+
+---------------+----------+
|
| 310 |
+
| learning_rate | 2.6e-05 |
|
| 311 |
+
+---------------+----------+
|
| 312 |
+
| epoch | 0.495238 |
|
| 313 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 314 |
+
2025-06-13 18:55:17,523 - INFO -
|
| 315 |
+
[36m🚂 Training Metrics (Step 140) 🚂
|
| 316 |
+
+---------------+----------+
|
| 317 |
+
| Metric | Value |
|
| 318 |
+
+===============+==========+
|
| 319 |
+
| loss | 0.0233 |
|
| 320 |
+
+---------------+----------+
|
| 321 |
+
| grad_norm | 0.007428 |
|
| 322 |
+
+---------------+----------+
|
| 323 |
+
| learning_rate | 2.8e-05 |
|
| 324 |
+
+---------------+----------+
|
| 325 |
+
| epoch | 0.533333 |
|
| 326 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 327 |
+
2025-06-13 18:55:37,253 - INFO -
|
| 328 |
+
[36m🚂 Training Metrics (Step 150) 🚂
|
| 329 |
+
+---------------+----------+
|
| 330 |
+
| Metric | Value |
|
| 331 |
+
+===============+==========+
|
| 332 |
+
| loss | 0.0227 |
|
| 333 |
+
+---------------+----------+
|
| 334 |
+
| grad_norm | 0.025854 |
|
| 335 |
+
+---------------+----------+
|
| 336 |
+
| learning_rate | 3e-05 |
|
| 337 |
+
+---------------+----------+
|
| 338 |
+
| epoch | 0.571429 |
|
| 339 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 340 |
+
2025-06-13 18:55:56,990 - INFO -
|
| 341 |
+
[36m🚂 Training Metrics (Step 160) 🚂
|
| 342 |
+
+---------------+----------+
|
| 343 |
+
| Metric | Value |
|
| 344 |
+
+===============+==========+
|
| 345 |
+
| loss | 0.0218 |
|
| 346 |
+
+---------------+----------+
|
| 347 |
+
| grad_norm | 0.015084 |
|
| 348 |
+
+---------------+----------+
|
| 349 |
+
| learning_rate | 3.2e-05 |
|
| 350 |
+
+---------------+----------+
|
| 351 |
+
| epoch | 0.609524 |
|
| 352 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 353 |
+
2025-06-13 18:56:16,703 - INFO -
|
| 354 |
+
[36m🚂 Training Metrics (Step 170) 🚂
|
| 355 |
+
+---------------+----------+
|
| 356 |
+
| Metric | Value |
|
| 357 |
+
+===============+==========+
|
| 358 |
+
| loss | 0.0216 |
|
| 359 |
+
+---------------+----------+
|
| 360 |
+
| grad_norm | 0.011318 |
|
| 361 |
+
+---------------+----------+
|
| 362 |
+
| learning_rate | 3.4e-05 |
|
| 363 |
+
+---------------+----------+
|
| 364 |
+
| epoch | 0.647619 |
|
| 365 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 366 |
+
2025-06-13 18:56:36,435 - INFO -
|
| 367 |
+
[36m🚂 Training Metrics (Step 180) 🚂
|
| 368 |
+
+---------------+----------+
|
| 369 |
+
| Metric | Value |
|
| 370 |
+
+===============+==========+
|
| 371 |
+
| loss | 0.0231 |
|
| 372 |
+
+---------------+----------+
|
| 373 |
+
| grad_norm | 0.021338 |
|
| 374 |
+
+---------------+----------+
|
| 375 |
+
| learning_rate | 3.6e-05 |
|
| 376 |
+
+---------------+----------+
|
| 377 |
+
| epoch | 0.685714 |
|
| 378 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 379 |
+
2025-06-13 18:56:56,191 - INFO -
|
| 380 |
+
[36m🚂 Training Metrics (Step 190) 🚂
|
| 381 |
+
+---------------+----------+
|
| 382 |
+
| Metric | Value |
|
| 383 |
+
+===============+==========+
|
| 384 |
+
| loss | 0.0236 |
|
| 385 |
+
+---------------+----------+
|
| 386 |
+
| grad_norm | 0.004745 |
|
| 387 |
+
+---------------+----------+
|
| 388 |
+
| learning_rate | 3.8e-05 |
|
| 389 |
+
+---------------+----------+
|
| 390 |
+
| epoch | 0.72381 |
|
| 391 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 392 |
+
2025-06-13 18:57:15,942 - INFO -
|
| 393 |
+
[36m🚂 Training Metrics (Step 200) 🚂
|
| 394 |
+
+---------------+----------+
|
| 395 |
+
| Metric | Value |
|
| 396 |
+
+===============+==========+
|
| 397 |
+
| loss | 0.0231 |
|
| 398 |
+
+---------------+----------+
|
| 399 |
+
| grad_norm | 0.025924 |
|
| 400 |
+
+---------------+----------+
|
| 401 |
+
| learning_rate | 4e-05 |
|
| 402 |
+
+---------------+----------+
|
| 403 |
+
| epoch | 0.761905 |
|
| 404 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 405 |
+
2025-06-13 18:57:35,695 - INFO -
|
| 406 |
+
[36m🚂 Training Metrics (Step 210) 🚂
|
| 407 |
+
+---------------+----------+
|
| 408 |
+
| Metric | Value |
|
| 409 |
+
+===============+==========+
|
| 410 |
+
| loss | 0.0222 |
|
| 411 |
+
+---------------+----------+
|
| 412 |
+
| grad_norm | 0.007095 |
|
| 413 |
+
+---------------+----------+
|
| 414 |
+
| learning_rate | 4.2e-05 |
|
| 415 |
+
+---------------+----------+
|
| 416 |
+
| epoch | 0.8 |
|
| 417 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 418 |
+
2025-06-13 18:57:55,463 - INFO -
|
| 419 |
+
[36m🚂 Training Metrics (Step 220) 🚂
|
| 420 |
+
+---------------+----------+
|
| 421 |
+
| Metric | Value |
|
| 422 |
+
+===============+==========+
|
| 423 |
+
| loss | 0.0225 |
|
| 424 |
+
+---------------+----------+
|
| 425 |
+
| grad_norm | 0.012384 |
|
| 426 |
+
+---------------+----------+
|
| 427 |
+
| learning_rate | 4.4e-05 |
|
| 428 |
+
+---------------+----------+
|
| 429 |
+
| epoch | 0.838095 |
|
| 430 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 431 |
+
2025-06-13 18:58:15,215 - INFO -
|
| 432 |
+
[36m🚂 Training Metrics (Step 230) 🚂
|
| 433 |
+
+---------------+---------+
|
| 434 |
+
| Metric | Value |
|
| 435 |
+
+===============+=========+
|
| 436 |
+
| loss | 0.0238 |
|
| 437 |
+
+---------------+---------+
|
| 438 |
+
| grad_norm | 0.00828 |
|
| 439 |
+
+---------------+---------+
|
| 440 |
+
| learning_rate | 4.6e-05 |
|
| 441 |
+
+---------------+---------+
|
| 442 |
+
| epoch | 0.87619 |
|
| 443 |
+
+---------------+---------+[0m - [multilabel_classify.py:2188:on_log]
|
| 444 |
+
2025-06-13 18:58:34,964 - INFO -
|
| 445 |
+
[36m🚂 Training Metrics (Step 240) 🚂
|
| 446 |
+
+---------------+----------+
|
| 447 |
+
| Metric | Value |
|
| 448 |
+
+===============+==========+
|
| 449 |
+
| loss | 0.0222 |
|
| 450 |
+
+---------------+----------+
|
| 451 |
+
| grad_norm | 0.006233 |
|
| 452 |
+
+---------------+----------+
|
| 453 |
+
| learning_rate | 4.8e-05 |
|
| 454 |
+
+---------------+----------+
|
| 455 |
+
| epoch | 0.914286 |
|
| 456 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 457 |
+
2025-06-13 18:58:54,736 - INFO -
|
| 458 |
+
[36m🚂 Training Metrics (Step 250) 🚂
|
| 459 |
+
+---------------+----------+
|
| 460 |
+
| Metric | Value |
|
| 461 |
+
+===============+==========+
|
| 462 |
+
| loss | 0.0232 |
|
| 463 |
+
+---------------+----------+
|
| 464 |
+
| grad_norm | 0.011117 |
|
| 465 |
+
+---------------+----------+
|
| 466 |
+
| learning_rate | 5e-05 |
|
| 467 |
+
+---------------+----------+
|
| 468 |
+
| epoch | 0.952381 |
|
| 469 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 470 |
+
2025-06-13 18:59:14,504 - INFO -
|
| 471 |
+
[36m🚂 Training Metrics (Step 260) 🚂
|
| 472 |
+
+---------------+----------+
|
| 473 |
+
| Metric | Value |
|
| 474 |
+
+===============+==========+
|
| 475 |
+
| loss | 0.023 |
|
| 476 |
+
+---------------+----------+
|
| 477 |
+
| grad_norm | 0.008961 |
|
| 478 |
+
+---------------+----------+
|
| 479 |
+
| learning_rate | 5.2e-05 |
|
| 480 |
+
+---------------+----------+
|
| 481 |
+
| epoch | 0.990476 |
|
| 482 |
+
+---------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 483 |
+
2025-06-13 18:59:19,754 - INFO - 💾 Model weights saved in safetensors format: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262 - [multilabel_classify.py:2445:_save]
|
| 484 |
+
2025-06-13 18:59:19,756 - INFO - ⚙️ Config saved in checkpoint: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262 - [multilabel_classify.py:2450:_save]
|
| 485 |
+
2025-06-13 18:59:19,757 - INFO - 📋 Saved files in ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262:
|
| 486 |
+
+---------+-------------------+------------+
|
| 487 |
+
| Index | Saved File | Size |
|
| 488 |
+
+=========+===================+============+
|
| 489 |
+
| 1 | training_args.bin | 0.01 MB |
|
| 490 |
+
+---------+-------------------+------------+
|
| 491 |
+
| 2 | model.safetensors | 4600.97 MB |
|
| 492 |
+
+---------+-------------------+------------+
|
| 493 |
+
| 3 | config.json | 0.00 MB |
|
| 494 |
+
+---------+-------------------+------------+ - [multilabel_classify.py:2467:_save]
|
| 495 |
+
2025-06-13 18:59:20,381 - INFO - Removing 'token_type_ids' from eval_dataset as they are not needed. - [multilabel_classify.py:2352:evaluate]
|
| 496 |
+
2025-06-13 19:12:32,601 - INFO -
|
| 497 |
+
[33m🔍 Evaluation Metrics 🔍
|
| 498 |
+
+-------------------------------+----------+
|
| 499 |
+
| Metric | Value |
|
| 500 |
+
+===============================+==========+
|
| 501 |
+
| eval_f1_micro | 0 |
|
| 502 |
+
+-------------------------------+----------+
|
| 503 |
+
| eval_f1_macro | 0 |
|
| 504 |
+
+-------------------------------+----------+
|
| 505 |
+
| eval_precision_at_5 | 0.274921 |
|
| 506 |
+
+-------------------------------+----------+
|
| 507 |
+
| eval_recall_at_5 | 0.063731 |
|
| 508 |
+
+-------------------------------+----------+
|
| 509 |
+
| eval_precision_at_8 | 0.253956 |
|
| 510 |
+
+-------------------------------+----------+
|
| 511 |
+
| eval_recall_at_8 | 0.090858 |
|
| 512 |
+
+-------------------------------+----------+
|
| 513 |
+
| eval_precision_at_15 | 0.190533 |
|
| 514 |
+
+-------------------------------+----------+
|
| 515 |
+
| eval_recall_at_15 | 0.122413 |
|
| 516 |
+
+-------------------------------+----------+
|
| 517 |
+
| eval_rare_f1_micro | 0 |
|
| 518 |
+
+-------------------------------+----------+
|
| 519 |
+
| eval_rare_f1_macro | 0 |
|
| 520 |
+
+-------------------------------+----------+
|
| 521 |
+
| eval_rare_precision | 0 |
|
| 522 |
+
+-------------------------------+----------+
|
| 523 |
+
| eval_rare_recall | 0 |
|
| 524 |
+
+-------------------------------+----------+
|
| 525 |
+
| eval_rare_precision_at_5 | 0.003718 |
|
| 526 |
+
+-------------------------------+----------+
|
| 527 |
+
| eval_rare_recall_at_5 | 0.001292 |
|
| 528 |
+
+-------------------------------+----------+
|
| 529 |
+
| eval_rare_precision_at_8 | 0.004302 |
|
| 530 |
+
+-------------------------------+----------+
|
| 531 |
+
| eval_rare_recall_at_8 | 0.002289 |
|
| 532 |
+
+-------------------------------+----------+
|
| 533 |
+
| eval_rare_precision_at_15 | 0.004905 |
|
| 534 |
+
+-------------------------------+----------+
|
| 535 |
+
| eval_rare_recall_at_15 | 0.00478 |
|
| 536 |
+
+-------------------------------+----------+
|
| 537 |
+
| eval_not_rare_f1_micro | 0 |
|
| 538 |
+
+-------------------------------+----------+
|
| 539 |
+
| eval_not_rare_f1_macro | 0 |
|
| 540 |
+
+-------------------------------+----------+
|
| 541 |
+
| eval_not_rare_precision | 0 |
|
| 542 |
+
+-------------------------------+----------+
|
| 543 |
+
| eval_not_rare_recall | 0 |
|
| 544 |
+
+-------------------------------+----------+
|
| 545 |
+
| eval_not_rare_precision_at_5 | 0.274209 |
|
| 546 |
+
+-------------------------------+----------+
|
| 547 |
+
| eval_not_rare_recall_at_5 | 0.168014 |
|
| 548 |
+
+-------------------------------+----------+
|
| 549 |
+
| eval_not_rare_precision_at_8 | 0.254005 |
|
| 550 |
+
+-------------------------------+----------+
|
| 551 |
+
| eval_not_rare_recall_at_8 | 0.239598 |
|
| 552 |
+
+-------------------------------+----------+
|
| 553 |
+
| eval_not_rare_precision_at_15 | 0.190585 |
|
| 554 |
+
+-------------------------------+----------+
|
| 555 |
+
| eval_not_rare_recall_at_15 | 0.324765 |
|
| 556 |
+
+-------------------------------+----------+
|
| 557 |
+
| eval_loss | 0.020932 |
|
| 558 |
+
+-------------------------------+----------+[0m - [multilabel_classify.py:2207:on_evaluate]
|
| 559 |
+
2025-06-13 19:12:36,537 - INFO - 💾 Model weights saved in safetensors format: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262 - [multilabel_classify.py:2445:_save]
|
| 560 |
+
2025-06-13 19:12:36,538 - INFO - ⚙️ Config saved in checkpoint: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262 - [multilabel_classify.py:2450:_save]
|
| 561 |
+
2025-06-13 19:12:36,540 - INFO - 📋 Saved files in ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262:
|
| 562 |
+
+---------+--------------------+------------+
|
| 563 |
+
| Index | Saved File | Size |
|
| 564 |
+
+=========+====================+============+
|
| 565 |
+
| 1 | training_args.bin | 0.01 MB |
|
| 566 |
+
+---------+--------------------+------------+
|
| 567 |
+
| 2 | optimizer.pt | 1308.77 MB |
|
| 568 |
+
+---------+--------------------+------------+
|
| 569 |
+
| 3 | model.safetensors | 4600.97 MB |
|
| 570 |
+
+---------+--------------------+------------+
|
| 571 |
+
| 4 | scaler.pt | 0.00 MB |
|
| 572 |
+
+---------+--------------------+------------+
|
| 573 |
+
| 5 | config.json | 0.00 MB |
|
| 574 |
+
+---------+--------------------+------------+
|
| 575 |
+
| 6 | scheduler.pt | 0.00 MB |
|
| 576 |
+
+---------+--------------------+------------+
|
| 577 |
+
| 7 | trainer_state.json | 0.00 MB |
|
| 578 |
+
+---------+--------------------+------------+
|
| 579 |
+
| 8 | rng_state.pth | 0.01 MB |
|
| 580 |
+
+---------+--------------------+------------+ - [multilabel_classify.py:2467:_save]
|
| 581 |
+
2025-06-13 19:12:37,957 - INFO - 📂 Loading best model from ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262 - [multilabel_classify.py:2519:_load_best_model]
|
| 582 |
+
2025-06-13 19:12:37,957 - INFO - 🖥️ Model is on device: cuda:0 - [multilabel_classify.py:2529:_load_best_model]
|
| 583 |
+
2025-06-13 19:12:38,014 - INFO - 🔑 Key order comparison:
|
| 584 |
+
+---------+--------------------------------------------+--------------------------------------------------------------------------------------+
|
| 585 |
+
| Index | Saved state_dict Keys | Model state_dict Keys |
|
| 586 |
+
+=========+============================================+======================================================================================+
|
| 587 |
+
| 1 | attention.in_proj_bias | boost_mul |
|
| 588 |
+
+---------+--------------------------------------------+--------------------------------------------------------------------------------------+
|
| 589 |
+
| 2 | attention.in_proj_weight | boost_add |
|
| 590 |
+
+---------+--------------------------------------------+--------------------------------------------------------------------------------------+
|
| 591 |
+
| 3 | attention.out_proj.bias | base_model.base_model.model.model.embed_tokens.weight |
|
| 592 |
+
+---------+--------------------------------------------+--------------------------------------------------------------------------------------+
|
| 593 |
+
| 4 | attention.out_proj.weight | base_model.base_model.model.model.layers.0.self_attn.q_proj.base_layer.weight |
|
| 594 |
+
+---------+--------------------------------------------+--------------------------------------------------------------------------------------+
|
| 595 |
+
| 5 | base_model.base_model.model.lm_head.weight | base_model.base_model.model.model.layers.0.self_attn.q_proj.base_layer.weight.absmax |
|
| 596 |
+
+---------+--------------------------------------------+--------------------------------------------------------------------------------------+ - [multilabel_classify.py:2553:_load_best_model]
|
| 597 |
+
2025-06-13 19:12:39,020 - INFO - ✅ Loaded best model weights from ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/checkpoint-262/model.safetensors - [multilabel_classify.py:2570:_load_best_model]
|
| 598 |
+
2025-06-13 19:12:39,059 - INFO - ✔️ Weight for boost_mul matches between saved and loaded state_dict - [multilabel_classify.py:2582:_load_best_model]
|
| 599 |
+
2025-06-13 19:12:39,091 - INFO - ✔️ Weight for boost_add matches between saved and loaded state_dict - [multilabel_classify.py:2582:_load_best_model]
|
| 600 |
+
2025-06-13 19:12:39,108 - INFO -
|
| 601 |
+
[36m🚂 Training Metrics (Step 262) 🚂
|
| 602 |
+
+--------------------------+----------+
|
| 603 |
+
| Metric | Value |
|
| 604 |
+
+==========================+==========+
|
| 605 |
+
| train_runtime | 1319.86 |
|
| 606 |
+
+--------------------------+----------+
|
| 607 |
+
| train_samples_per_second | 6.359 |
|
| 608 |
+
+--------------------------+----------+
|
| 609 |
+
| train_steps_per_second | 0.199 |
|
| 610 |
+
+--------------------------+----------+
|
| 611 |
+
| total_flos | 0 |
|
| 612 |
+
+--------------------------+----------+
|
| 613 |
+
| train_loss | 0.062579 |
|
| 614 |
+
+--------------------------+----------+
|
| 615 |
+
| epoch | 0.998095 |
|
| 616 |
+
+--------------------------+----------+[0m - [multilabel_classify.py:2188:on_log]
|
| 617 |
+
2025-06-13 19:12:39,108 - INFO - ✨ Training Completed! ✨ - [multilabel_classify.py:2061:on_train_end]
|
| 618 |
+
2025-06-13 19:12:39,183 - INFO - 📊 Training loss plot saved as '../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/train_loss_plot.png' - [multilabel_classify.py:2257:on_train_end]
|
| 619 |
+
2025-06-13 19:12:39,237 - INFO - 📊 Evaluation loss plot saved as '../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/eval_loss_plot.png' - [multilabel_classify.py:2271:on_train_end]
|
| 620 |
+
2025-06-13 19:12:39,297 - INFO - 📊 Evaluation metric plot saved as '../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b/eval_precision_at_15_plot.png' - [multilabel_classify.py:2292:on_train_end]
|
| 621 |
+
2025-06-13 19:12:39,298 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:101:log_section]
|
| 622 |
+
2025-06-13 19:12:39,298 - INFO - + ✨ MODEL SAVING + - [multilabel_classify.py:102:log_section]
|
| 623 |
+
2025-06-13 19:12:39,298 - INFO - ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - [multilabel_classify.py:105:log_section]
|
| 624 |
+
2025-06-13 19:12:39,298 - INFO - 💾 Saving trained model and pushing to Hugging Face Hub... - [multilabel_classify.py:4069:main]
|
| 625 |
+
2025-06-13 19:12:39,298 - INFO - 📁 Creating/using output directory: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:3045:save_and_push]
|
| 626 |
+
2025-06-13 19:12:40,623 - INFO - 💾 Model weights saved in safetensors format: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:2445:_save]
|
| 627 |
+
2025-06-13 19:12:40,625 - INFO - ⚙️ Config saved in checkpoint: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:2450:_save]
|
| 628 |
+
2025-06-13 19:12:40,626 - INFO - 📋 Saved files in ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b:
|
| 629 |
+
+---------+-------------------------------+------------+
|
| 630 |
+
| Index | Saved File | Size |
|
| 631 |
+
+=========+===============================+============+
|
| 632 |
+
| 1 | eval_loss_plot.png | 0.02 MB |
|
| 633 |
+
+---------+-------------------------------+------------+
|
| 634 |
+
| 2 | training_args.bin | 0.01 MB |
|
| 635 |
+
+---------+-------------------------------+------------+
|
| 636 |
+
| 3 | model.safetensors | 4600.97 MB |
|
| 637 |
+
+---------+-------------------------------+------------+
|
| 638 |
+
| 4 | config.json | 0.00 MB |
|
| 639 |
+
+---------+-------------------------------+------------+
|
| 640 |
+
| 5 | train_loss_plot.png | 0.02 MB |
|
| 641 |
+
+---------+-------------------------------+------------+
|
| 642 |
+
| 6 | eval_precision_at_15_plot.png | 0.03 MB |
|
| 643 |
+
+---------+-------------------------------+------------+ - [multilabel_classify.py:2467:_save]
|
| 644 |
+
2025-06-13 19:12:44,632 - INFO - 💾 Model weights saved in safetensors format: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:2445:_save]
|
| 645 |
+
2025-06-13 19:12:44,634 - INFO - ⚙️ Config saved in checkpoint: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:2450:_save]
|
| 646 |
+
2025-06-13 19:12:44,635 - INFO - 📋 Saved files in ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b:
|
| 647 |
+
+---------+-------------------------------+------------+
|
| 648 |
+
| Index | Saved File | Size |
|
| 649 |
+
+=========+===============================+============+
|
| 650 |
+
| 1 | eval_loss_plot.png | 0.02 MB |
|
| 651 |
+
+---------+-------------------------------+------------+
|
| 652 |
+
| 2 | training_args.bin | 0.01 MB |
|
| 653 |
+
+---------+-------------------------------+------------+
|
| 654 |
+
| 3 | model.safetensors | 4600.97 MB |
|
| 655 |
+
+---------+-------------------------------+------------+
|
| 656 |
+
| 4 | config.json | 0.00 MB |
|
| 657 |
+
+---------+-------------------------------+------------+
|
| 658 |
+
| 5 | train_loss_plot.png | 0.02 MB |
|
| 659 |
+
+---------+-------------------------------+------------+
|
| 660 |
+
| 6 | eval_precision_at_15_plot.png | 0.03 MB |
|
| 661 |
+
+---------+-------------------------------+------------+ - [multilabel_classify.py:2467:_save]
|
| 662 |
+
2025-06-13 19:14:09,684 - INFO - 💾 Model saved to: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:3049:save_and_push]
|
| 663 |
+
2025-06-13 19:14:09,714 - INFO - 🖌️ Tokenizer saved to: ../tmp/MIMIC4_DEMO/mimic4_classify_mistral7b - [multilabel_classify.py:3053:save_and_push]
|