README.md

---
license: other
license_name: llama-3
license_link: https://huggingface.co./meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE

base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- generated_from_trainer
model-index:
- name: workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct
  results: []
---

<p align="center">
  <img width=400 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kg3QjQOde0X743csGJT-f.png" alt="Suzume - a Japanese tree sparrow"/>
</p>

# Suzume

[[Paper](https://arxiv.org/abs/2405.12612)] [[Dataset](https://huggingface.co./datasets/lightblue/tagengo-gpt4)]

This Suzume 8B, a Japanese finetune of Llama 3.

Llama 3 has exhibited excellent performance on many English language benchmarks. 
However, it also seemingly been finetuned on mostly English data, meaning that it will respond in English, even if prompted in Japanese.

We have fine-tuned Llama 3 on almost 3,000 Japanese conversations meaning that this model has the smarts of Llama 3 but has the added ability to chat in Japanese.

Please feel free to comment on this model and give us feedback in the Community tab!

# How to use

You can use the GGUF using LM Studio

LM Studioで簡単に使えます！[こちら](https://note.com/masayuki_abe/n/nd65ed694eec0)は使い方を説明します。
LM Studioで「lightblue/suzume-llama-3-8B-japanese-gguf」を検索して下さい。


# Evaluation scores

We find that this is the best performing model in the 7/8B class of LLMs on a multitude of Japanese language benchmarks.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/2obyDbrjiNV3PGfwom6EI.png)

# Training data

We train on three sources of data to create this model

* [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja) - 669 conversations
    * A hand-edited dataset of nearly 700 conversations taken originally from translations of the [kunishou/hh-rlhf-49k-ja](https://huggingface.co./datasets/kunishou/hh-rlhf-49k-ja) dataset.
* [openchat/openchat_sharegpt4_dataset](https://huggingface.co./datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json) (Japanese conversations only) - 167 conversations
    * Conversations taken from humans talking to GPT-4
* lightblue/tagengo-gpt4 (Japanese prompts only) (Link coming soon!) - 2,482 conversations
    * Almost 2,500 diverse Japanese prompts sampled from [lmsys/lmsys-chat-1m](https://huggingface.co./datasets/lmsys/lmsys-chat-1m) and then used to prompt `gpt-4-0125-preview`

# Training config

[<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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /workspace/llm_training/axolotl/llama3-ja/openchat_megagon_lbgpt4_ja.json
    ds_type: json # see other options below
    type: sharegpt
    conversation: llama-3
dataset_prepared_path: /workspace/llm_training/axolotl/llama3-ja/prepared_openchat_megagon_lbgpt4_ja
val_set_size: 0.01
output_dir: /workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: False

use_wandb: true
wandb_project: axolotl
wandb_entity: peterd
wandb_name: openchat_megagon_lbgpt4_ja_8B_instruct

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>
```

</details><br>


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- total_eval_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.303         | 0.08  | 1    | 1.2664          |
| 1.4231        | 0.23  | 3    | 1.2409          |
| 1.1007        | 0.46  | 6    | 1.0264          |
| 1.0635        | 0.69  | 9    | 1.0154          |
| 1.0221        | 0.92  | 12   | 0.9555          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0

# How to cite

Please cite [this paper](https://arxiv.org/abs/2405.12612) when referencing this model.

```tex
@article{devine2024tagengo,
  title={Tagengo: A Multilingual Chat Dataset},
  author={Devine, Peter},
  journal={arXiv preprint arXiv:2405.12612},
  year={2024}
}
```

# Developer

Peter Devine - ([ptrdvn](https://huggingface.co./ptrdvn))