---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: /data1/model/llama2/meta-llama/Llama2-13b
model-index:
- name: elementary_math_qa_no_sys
  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. -->

# elementary_math_qa_no_sys

This model is a fine-tuned version of [/data1/model/llama2/meta-llama/Llama2-13b](https://huggingface.co//data1/model/llama2/meta-llama/Llama2-13b) on the elementary_math_qa_no_sys dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0705

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 24
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3438        | 0.05  | 50   | 0.3364          |
| 0.3021        | 0.09  | 100  | 0.3056          |
| 0.2676        | 0.14  | 150  | 0.2710          |
| 0.2679        | 0.18  | 200  | 0.2582          |
| 0.2448        | 0.23  | 250  | 0.2475          |
| 0.2355        | 0.28  | 300  | 0.2372          |
| 0.2335        | 0.32  | 350  | 0.2282          |
| 0.2226        | 0.37  | 400  | 0.2227          |
| 0.2098        | 0.42  | 450  | 0.2109          |
| 0.1839        | 0.46  | 500  | 0.2048          |
| 0.2008        | 0.51  | 550  | 0.1992          |
| 0.1945        | 0.55  | 600  | 0.2019          |
| 0.1891        | 0.6   | 650  | 0.1859          |
| 0.2015        | 0.65  | 700  | 0.1966          |
| 0.174         | 0.69  | 750  | 0.1801          |
| 0.1565        | 0.74  | 800  | 0.1762          |
| 0.1825        | 0.79  | 850  | 0.1717          |
| 0.1651        | 0.83  | 900  | 0.1682          |
| 0.1598        | 0.88  | 950  | 0.1598          |
| 0.1502        | 0.92  | 1000 | 0.1558          |
| 0.1599        | 0.97  | 1050 | 0.1465          |
| 0.0977        | 1.02  | 1100 | 0.1520          |
| 0.1166        | 1.06  | 1150 | 0.1403          |
| 0.0943        | 1.11  | 1200 | 0.1387          |
| 0.1007        | 1.16  | 1250 | 0.1311          |
| 0.1035        | 1.2   | 1300 | 0.1325          |
| 0.0842        | 1.25  | 1350 | 0.1309          |
| 0.1114        | 1.29  | 1400 | 0.1225          |
| 0.1047        | 1.34  | 1450 | 0.1184          |
| 0.0807        | 1.39  | 1500 | 0.1136          |
| 0.0846        | 1.43  | 1550 | 0.1200          |
| 0.0737        | 1.48  | 1600 | 0.1145          |
| 0.0844        | 1.52  | 1650 | 0.1037          |
| 0.0809        | 1.57  | 1700 | 0.0940          |
| 0.0718        | 1.62  | 1750 | 0.0931          |
| 0.0687        | 1.66  | 1800 | 0.0930          |
| 0.0629        | 1.71  | 1850 | 0.0969          |
| 0.0852        | 1.76  | 1900 | 0.0872          |
| 0.0622        | 1.8   | 1950 | 0.0849          |
| 0.0653        | 1.85  | 2000 | 0.0831          |
| 0.0507        | 1.89  | 2050 | 0.0829          |
| 0.0518        | 1.94  | 2100 | 0.0785          |
| 0.0566        | 1.99  | 2150 | 0.0750          |
| 0.0193        | 2.03  | 2200 | 0.0837          |
| 0.0233        | 2.08  | 2250 | 0.0766          |
| 0.0249        | 2.13  | 2300 | 0.0829          |
| 0.0217        | 2.17  | 2350 | 0.0824          |
| 0.0233        | 2.22  | 2400 | 0.0735          |
| 0.0192        | 2.26  | 2450 | 0.0767          |
| 0.0207        | 2.31  | 2500 | 0.0794          |
| 0.0232        | 2.36  | 2550 | 0.0843          |
| 0.0295        | 2.4   | 2600 | 0.0800          |
| 0.0185        | 2.45  | 2650 | 0.0777          |
| 0.0178        | 2.5   | 2700 | 0.0767          |
| 0.0245        | 2.54  | 2750 | 0.0717          |
| 0.0226        | 2.59  | 2800 | 0.0774          |
| 0.0222        | 2.63  | 2850 | 0.0671          |
| 0.0194        | 2.68  | 2900 | 0.0666          |
| 0.0162        | 2.73  | 2950 | 0.0713          |
| 0.0184        | 2.77  | 3000 | 0.0740          |
| 0.0227        | 2.82  | 3050 | 0.0675          |
| 0.0176        | 2.87  | 3100 | 0.0701          |
| 0.034         | 2.91  | 3150 | 0.0675          |
| 0.0148        | 2.96  | 3200 | 0.0688          |
| 0.014         | 3.0   | 3250 | 0.0673          |
| 0.0178        | 3.05  | 3300 | 0.0719          |
| 0.0059        | 3.1   | 3350 | 0.0734          |
| 0.0069        | 3.14  | 3400 | 0.0764          |
| 0.0074        | 3.19  | 3450 | 0.0818          |
| 0.009         | 3.23  | 3500 | 0.0705          |
| 0.0048        | 3.28  | 3550 | 0.0735          |
| 0.005         | 3.33  | 3600 | 0.0705          |
| 0.0073        | 3.37  | 3650 | 0.0724          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2