Model save

README.md

@@ -0,0 +1,115 @@
+---
+library_name: peft
+license: gemma
+base_model: google/gemma-2b
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: results
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# results
+
+This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5635
+- Accuracy: 0.7531
+- Report:               precision    recall  f1-score   support
+
+     absence       0.75      0.84      0.79        45
+    presence       0.77      0.64      0.70        36
+
+    accuracy                           0.75        81
+   macro avg       0.76      0.74      0.74        81
+weighted avg       0.75      0.75      0.75        81
+
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Report                                                                                                                                                                                                                                                                                                                                 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 48   | 0.6049          | 0.6420   |               precision    recall  f1-score   support
+
+     absence       0.75      0.53      0.62        45
+    presence       0.57      0.78      0.66        36
+
+    accuracy                           0.64        81
+   macro avg       0.66      0.66      0.64        81
+weighted avg       0.67      0.64      0.64        81
+ |
+| No log        | 2.0   | 96   | 0.5779          | 0.7531   |               precision    recall  f1-score   support
+
+     absence       0.75      0.84      0.79        45
+    presence       0.77      0.64      0.70        36
+
+    accuracy                           0.75        81
+   macro avg       0.76      0.74      0.74        81
+weighted avg       0.75      0.75      0.75        81
+ |
+| No log        | 3.0   | 144  | 0.5692          | 0.7531   |               precision    recall  f1-score   support
+
+     absence       0.75      0.84      0.79        45
+    presence       0.77      0.64      0.70        36
+
+    accuracy                           0.75        81
+   macro avg       0.76      0.74      0.74        81
+weighted avg       0.75      0.75      0.75        81
+ |
+| No log        | 4.0   | 192  | 0.5676          | 0.7531   |               precision    recall  f1-score   support
+
+     absence       0.75      0.84      0.79        45
+    presence       0.77      0.64      0.70        36
+
+    accuracy                           0.75        81
+   macro avg       0.76      0.74      0.74        81
+weighted avg       0.75      0.75      0.75        81
+ |
+| No log        | 5.0   | 240  | 0.5635          | 0.7531   |               precision    recall  f1-score   support
+
+     absence       0.75      0.84      0.79        45
+    presence       0.77      0.64      0.70        36
+
+    accuracy                           0.75        81
+   macro avg       0.76      0.74      0.74        81
+weighted avg       0.75      0.75      0.75        81
+ |
+
+
+### Framework versions
+
+- PEFT 0.15.2
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.1

runs/May20_22-20-09_6d8491dd7888/events.out.tfevents.1747779932.6d8491dd7888.631.2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9f7168cbb9b493f4b6d773d217f4d00a76ad71b135717a37f76423f2094106f
+size 821