Model save

README.md

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+---
+base_model: meta-llama/Llama-3.2-1B
+library_name: peft
+license: llama3.2
+metrics:
+- accuracy
+tags:
+- generated_from_trainer
+model-index:
+- name: llama3.2-finetuned-newsclassify
+  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. -->
+
+# llama3.2-finetuned-newsclassify
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0941
+- Balanced Accuracy: 0.984
+- Accuracy: 0.984
+- F1-score: 0.9839
+- Classification-report:               precision    recall  f1-score   support
+
+           0       1.00      0.92      0.96        50
+           1       1.00      1.00      1.00        50
+           2       0.98      1.00      0.99        50
+           3       1.00      1.00      1.00        50
+           4       0.94      1.00      0.97        50
+
+    accuracy                           0.98       250
+   macro avg       0.98      0.98      0.98       250
+weighted avg       0.98      0.98      0.98       250
+
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use 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: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Balanced Accuracy | Accuracy | F1-score | Classification-report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 0.001         | 1.0   | 157  | 0.0941          | 0.984             | 0.984    | 0.9839   |               precision    recall  f1-score   support
+
+           0       1.00      0.92      0.96        50
+           1       1.00      1.00      1.00        50
+           2       0.98      1.00      0.99        50
+           3       1.00      1.00      1.00        50
+           4       0.94      1.00      0.97        50
+
+    accuracy                           0.98       250
+   macro avg       0.98      0.98      0.98       250
+weighted avg       0.98      0.98      0.98       250
+ |
+
+
+### Framework versions
+
+- PEFT 0.13.2
+- Transformers 4.47.0.dev0
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.2
+- Tokenizers 0.20.1

tokenizer.json

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-size 17210018
+oid sha256:8455cd05329f9cb9895e200605934b714e7fc55873b9969c77d1c3c01ccc60e0
+size 17210188