README.md

---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama-2-7b-hf-zero-shot-prompt
  results: []
license: mit
datasets:
- niting3c/Malicious_packets_subset
- niting3c/Malicious_packets
metrics:
  - type: "accuracy"
    value: 0.546
    name: "Accuracy"

  - type: "recall"
    value: 0.098
    name: "recall"

  - type: "precision" 
    value: 0.9423076923076923,
    name: "precision"
  
  - type: "f1"
    value: 0.17753623188405795
    name: "f1"
    


pipeline_tag: text-classification
---

<!-- 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. -->

# llama-2-7b-hf-zero-shot-prompt

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3135
- `{'accuracy': 0.546, 'recall': 0.098, 'precision': 0.9423076923076923, 'f1': 0.17753623188405795, 'total_time_in_seconds': 2308.70937472, 'samples_per_second': 0.4331424348815146, 'latency_in_seconds': 2.3087093747200003}`

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 100
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.22  | 10   | 1.3991          |
| No log        | 0.44  | 20   | 1.3609          |
| No log        | 0.67  | 30   | 1.3327          |
| 1.4726        | 0.89  | 40   | 1.3135          |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3