rishavranaut/llama2-7B_MT

README.md

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+---
+license: llama2
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: meta-llama/Llama-2-7b-hf
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: llama2-7B_MT
+  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. -->
+
+# llama2-7B_MT
+
+This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7960
+- Accuracy: 0.8317
+- Precision: 0.8541
+- Recall: 0.8
+- F1 score: 0.8262
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 0.693         | 0.25  | 200  | 0.6029          | 0.7617   | 0.8398    | 0.6467 | 0.7307   |
+| 0.5602        | 0.5   | 400  | 0.6130          | 0.7733   | 0.8661    | 0.6467 | 0.7405   |
+| 0.5364        | 0.75  | 600  | 0.4880          | 0.785    | 0.7714    | 0.81   | 0.7902   |
+| 0.5136        | 1.0   | 800  | 0.5408          | 0.7717   | 0.8327    | 0.68   | 0.7486   |
+| 0.3945        | 1.25  | 1000 | 0.6649          | 0.7683   | 0.9215    | 0.5867 | 0.7169   |
+| 0.3574        | 1.5   | 1200 | 0.5797          | 0.7767   | 0.7413    | 0.85   | 0.7919   |
+| 0.3927        | 1.75  | 1400 | 0.4764          | 0.8267   | 0.8333    | 0.8167 | 0.8249   |
+| 0.3584        | 2.0   | 1600 | 0.4186          | 0.8267   | 0.8475    | 0.7967 | 0.8213   |
+| 0.2488        | 2.25  | 1800 | 0.4973          | 0.8317   | 0.8755    | 0.7733 | 0.8212   |
+| 0.2519        | 2.5   | 2000 | 0.5590          | 0.8217   | 0.8814    | 0.7433 | 0.8065   |
+| 0.2424        | 2.75  | 2200 | 0.6088          | 0.8217   | 0.8587    | 0.77   | 0.8120   |
+| 0.2517        | 3.0   | 2400 | 0.5793          | 0.8317   | 0.8964    | 0.75   | 0.8167   |
+| 0.1178        | 3.25  | 2600 | 0.6630          | 0.8183   | 0.8498    | 0.7733 | 0.8098   |
+| 0.1058        | 3.5   | 2800 | 0.9330          | 0.8167   | 0.8958    | 0.7167 | 0.7963   |
+| 0.098         | 3.75  | 3000 | 0.7077          | 0.82     | 0.8137    | 0.83   | 0.8218   |
+| 0.0875        | 4.0   | 3200 | 0.6751          | 0.82     | 0.8288    | 0.8067 | 0.8176   |
+| 0.0251        | 4.25  | 3400 | 0.7202          | 0.8283   | 0.8339    | 0.82   | 0.8269   |
+| 0.0202        | 4.5   | 3600 | 0.7859          | 0.83     | 0.8587    | 0.79   | 0.8229   |
+| 0.0196        | 4.75  | 3800 | 0.8298          | 0.8333   | 0.8650    | 0.79   | 0.8258   |
+| 0.0174        | 5.0   | 4000 | 0.7960          | 0.8317   | 0.8541    | 0.8    | 0.8262   |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.44.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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