transformers llama text-generation generated_from_trainer base_model:elyza/ELYZA-japanese-Llama-2-7b-instruct autotrain_compatible endpoints_compatible text-generation-inference 4-bit bitsandbytes region:us
---
base_model: elyza/ELYZA-japanese-Llama-2-7b-instruct
tags:
- generated_from_trainer
model-index:
- name: medusa-1.0-ELYZA-japanese-Llama-2-7b-instruct
  results: []
---

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Access-AI-Collective/axolotl)
# medusa-1.0-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: 1.8838

## Model description

This is a Medusa-1 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4268        | 0.06  | 40   | 2.4129          |
| 2.204         | 0.11  | 80   | 2.2328          |
| 2.1341        | 0.17  | 120  | 2.1939          |
| 2.1774        | 0.23  | 160  | 2.1762          |
| 2.1331        | 0.28  | 200  | 2.1652          |
| 2.1485        | 0.34  | 240  | 2.1537          |
| 2.1608        | 0.4   | 280  | 2.1485          |
| 2.0998        | 0.45  | 320  | 2.1340          |
| 2.1478        | 0.51  | 360  | 2.1233          |
| 2.1374        | 0.57  | 400  | 2.1165          |
| 2.0657        | 0.62  | 440  | 2.0984          |
| 2.1227        | 0.68  | 480  | 2.0834          |
| 2.0573        | 0.74  | 520  | 2.0739          |
| 2.0501        | 0.79  | 560  | 2.0602          |
| 2.0664        | 0.85  | 600  | 2.0431          |
| 2.0231        | 0.91  | 640  | 2.0277          |
| 2.0263        | 0.96  | 680  | 2.0119          |
| 1.8635        | 1.02  | 720  | 1.9990          |
| 1.8844        | 1.07  | 760  | 1.9905          |
| 1.8585        | 1.13  | 800  | 1.9796          |
| 1.8018        | 1.19  | 840  | 1.9728          |
| 1.8134        | 1.24  | 880  | 1.9595          |
| 1.7795        | 1.3   | 920  | 1.9498          |
| 1.7603        | 1.36  | 960  | 1.9371          |
| 1.8302        | 1.41  | 1000 | 1.9262          |
| 1.7909        | 1.47  | 1040 | 1.9172          |
| 1.7787        | 1.53  | 1080 | 1.9083          |
| 1.7476        | 1.58  | 1120 | 1.9010          |
| 1.7897        | 1.64  | 1160 | 1.8945          |
| 1.7795        | 1.7   | 1200 | 1.8895          |
| 1.7329        | 1.75  | 1240 | 1.8864          |
| 1.7221        | 1.81  | 1280 | 1.8846          |
| 1.7624        | 1.87  | 1320 | 1.8840          |
| 1.793         | 1.92  | 1360 | 1.8838          |
| 1.7839        | 1.98  | 1400 | 1.8838          |


### Framework versions

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