---
library_name: peft
base_model: NousResearch/Llama-2-7b-hf
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: evaluation_model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# evaluation_model

This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7124
- Accuracy: 0.4667
- Precision: 0.4577
- Recall: 0.9559
- F1: 0.6190

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.9829 | 43   | 0.9195          | 0.5467   | 0.0       | 0.0    | 0.0    |
| No log        | 1.9943 | 87   | 0.6833          | 0.5667   | 0.5172    | 0.6618 | 0.5806 |
| No log        | 2.9829 | 130  | 0.6898          | 0.5267   | 0.4884    | 0.9265 | 0.6396 |
| 0.8708        | 3.9943 | 174  | 0.6775          | 0.5667   | 0.5149    | 0.7647 | 0.6154 |
| 0.8708        | 4.9371 | 215  | 0.7124          | 0.4667   | 0.4577    | 0.9559 | 0.6190 |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3