---
base_model: elyza/ELYZA-japanese-Llama-2-7b-instruct
tags:
- generated_from_trainer
model-index:
- name: medusa-ELYZA-japanese-Llama-2-7b-instruct
  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. -->

[<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)
# medusa-ELYZA-japanese-Llama-2-7b-instruct

This model is a fine-tuned version of [elyza/ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3564

## Model description

This is a Medusa-2 created using [Medusa](https://github.com/FasterDecoding/Medusa).

## Intended uses & limitations

- [【Orion-14B Series】 Models Community License Agreement](https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/ModelsCommunityLicenseAgreement)

## Training and evaluation data

- [shi3z/ja_conv_wikipedia_orion14B_100K](https://huggingface.co/datasets/shi3z/ja_conv_wikipedia_orion14B_100K)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.684         | 0.06  | 40   | 2.7430          |
| 2.5302        | 0.11  | 80   | 2.6693          |
| 2.486         | 0.17  | 120  | 2.6273          |
| 2.557         | 0.23  | 160  | 2.6020          |
| 2.4913        | 0.28  | 200  | 2.5868          |
| 2.5317        | 0.34  | 240  | 2.5646          |
| 2.4795        | 0.4   | 280  | 2.5521          |
| 2.4221        | 0.45  | 320  | 2.5359          |
| 2.4464        | 0.51  | 360  | 2.5231          |
| 2.4534        | 0.57  | 400  | 2.5095          |
| 2.4685        | 0.62  | 440  | 2.4967          |
| 2.4575        | 0.68  | 480  | 2.4849          |
| 2.4299        | 0.74  | 520  | 2.4771          |
| 2.459         | 0.79  | 560  | 2.4604          |
| 2.4585        | 0.85  | 600  | 2.4527          |
| 2.4832        | 0.91  | 640  | 2.4425          |
| 2.4255        | 0.96  | 680  | 2.4285          |
| 2.2209        | 1.02  | 720  | 2.4312          |
| 2.3142        | 1.07  | 760  | 2.4288          |
| 2.1961        | 1.13  | 800  | 2.4252          |
| 2.1394        | 1.19  | 840  | 2.4194          |
| 2.2005        | 1.24  | 880  | 2.4093          |
| 2.0748        | 1.3   | 920  | 2.4003          |
| 2.109         | 1.36  | 960  | 2.3935          |
| 2.2209        | 1.41  | 1000 | 2.3856          |
| 2.1938        | 1.47  | 1040 | 2.3786          |
| 2.1056        | 1.53  | 1080 | 2.3716          |
| 2.0948        | 1.58  | 1120 | 2.3674          |
| 2.218         | 1.64  | 1160 | 2.3629          |
| 2.17          | 1.7   | 1200 | 2.3601          |
| 2.1084        | 1.75  | 1240 | 2.3590          |
| 2.0446        | 1.81  | 1280 | 2.3567          |
| 2.1517        | 1.87  | 1320 | 2.3572          |
| 2.2342        | 1.92  | 1360 | 2.3565          |
| 2.1552        | 1.98  | 1400 | 2.3564          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.14.1