---
tags:
- generated_from_trainer
model-index:
- name: no_robots-alpaca
  results: []
license: cc-by-nc-4.0
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# no_robots-alpaca

This lora was trained with [Doctor-Shotgun/no-robots-sharegpt](https://huggingface.co./datasets/Doctor-Shotgun/no-robots-sharegpt) dataset on [TheBloke/Llama-2-13B-fp16](https://huggingface.co./TheBloke/Llama-2-13B-fp16).
It achieves the following results on the evaluation set:
- Loss: 1.6087

## Model description

The LoRA was trained on [Doctor-Shotgun/no-robots-sharegpt](https://huggingface.co./datasets/Doctor-Shotgun/no-robots-sharegpt), a ShareGPT converted dataset from the OG [HuggingFaceH4/no_robots](https://huggingface.co./datasets/HuggingFaceH4/no_robots) but with Alpaca prompting. 

## Prompt template: Alpaca

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00065
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5523        | 0.0   | 1    | 1.5476          |
| 1.2139        | 0.1   | 42   | 1.5008          |
| 1.6348        | 0.2   | 84   | 1.4968          |
| 1.6498        | 0.3   | 126  | 1.4962          |
| 1.5645        | 0.4   | 168  | 1.4983          |
| 1.6487        | 0.5   | 210  | 1.4981          |
| 1.6147        | 0.6   | 252  | 1.4965          |
| 1.3048        | 0.7   | 294  | 1.4973          |
| 1.6205        | 0.8   | 336  | 1.5007          |
| 1.6045        | 0.9   | 378  | 1.5003          |
| 1.5781        | 1.0   | 420  | 1.5013          |
| 1.4807        | 1.09  | 462  | 1.5492          |
| 1.0541        | 1.19  | 504  | 1.5596          |
| 1.2337        | 1.29  | 546  | 1.5789          |
| 0.9719        | 1.39  | 588  | 1.5859          |
| 1.2189        | 1.49  | 630  | 1.5959          |
| 1.2566        | 1.59  | 672  | 1.5968          |
| 0.7049        | 1.69  | 714  | 1.5987          |
| 1.2133        | 1.79  | 756  | 1.5907          |
| 1.0327        | 1.89  | 798  | 1.6087          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1

If you want to support me, you can [here](https://ko-fi.com/undiai).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_Undi95__Llama2-13B-no_robots-alpaca-lora)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 46.55   |
| ARC (25-shot)         | 58.87          |
| HellaSwag (10-shot)   | 82.43    |
| MMLU (5-shot)         | 53.11         |
| TruthfulQA (0-shot)   | 40.46   |
| Winogrande (5-shot)   | 75.3   |
| GSM8K (5-shot)        | 6.44        |
| DROP (3-shot)         | 9.26         |