metadata

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

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

This model is a fine-tuned version of 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