---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: qlora-adapter-Llama-2-7b-hf-databricks-dolly-15k
  results: []
---

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# qlora-adapter-Llama-2-7b-hf-databricks-dolly-15k

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 [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.

It achieves the following results on the evaluation set:
- Loss: 1.1313

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

Trained on RTX A5000 - 24GB GPU. The training took 3 hours 31 mins on the datasets with 12008 train samples and 1501 validation samples

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1584        | 0.08  | 1000  | 1.1782          |
| 1.0667        | 0.17  | 2000  | 1.1710          |
| 1.0662        | 0.25  | 3000  | 1.1599          |
| 1.0517        | 0.33  | 4000  | 1.1569          |
| 1.0479        | 0.42  | 5000  | 1.1502          |
| 1.0516        | 0.5   | 6000  | 1.1441          |
| 1.0612        | 0.58  | 7000  | 1.1397          |
| 1.0235        | 0.67  | 8000  | 1.1361          |
| 1.0259        | 0.75  | 9000  | 1.1339          |
| 1.0485        | 0.83  | 10000 | 1.1320          |
| 1.0406        | 0.92  | 11000 | 1.1314          |
| 1.0393        | 1.0   | 12000 | 1.1313          |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3