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---
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 |