---
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
license: llama2
tags:
- generated_from_trainer
model-index:
- name: llama2-7b-qlora-finetuned_1
  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-qlora-finetuned_1

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: 0.4910
- Model Preparation Time: 0.0048

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|
| 8.4619        | 0.1669 | 100  | 0.5918          | 0.0048                 |
| 0.5531        | 0.3339 | 200  | 0.5314          | 0.0048                 |
| 0.5311        | 0.5008 | 300  | 0.5164          | 0.0048                 |
| 0.5179        | 0.6677 | 400  | 0.5114          | 0.0048                 |
| 0.5168        | 0.8346 | 500  | 0.5072          | 0.0048                 |
| 0.5124        | 1.0016 | 600  | 0.5034          | 0.0048                 |
| 0.5053        | 1.1685 | 700  | 0.5003          | 0.0048                 |
| 0.5047        | 1.3354 | 800  | 0.5001          | 0.0048                 |
| 0.5008        | 1.5023 | 900  | 0.4967          | 0.0048                 |
| 0.4985        | 1.6693 | 1000 | 0.4969          | 0.0048                 |
| 0.4998        | 1.8362 | 1100 | 0.4941          | 0.0048                 |
| 0.4987        | 2.0031 | 1200 | 0.4978          | 0.0048                 |
| 0.4939        | 2.1701 | 1300 | 0.4933          | 0.0048                 |
| 0.4907        | 2.3370 | 1400 | 0.4923          | 0.0048                 |
| 0.4947        | 2.5039 | 1500 | 0.4910          | 0.0048                 |
| 0.4896        | 2.6708 | 1600 | 0.4901          | 0.0048                 |
| 0.4923        | 2.8378 | 1700 | 0.4896          | 0.0048                 |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0