README.md

---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
library_name: transformers
inference: false
---
# llm-jp-3-8x1.8b-instruct3

LLM-jp-3 is the series of large language models developed by the [Research and Development Center for Large Language Models](https://llmc.nii.ac.jp/) at the [National Institute of Informatics](https://www.nii.ac.jp/en/).

This repository provides the **llm-jp-3-8x1.8b-instruct3** model.
For an overview of the LLM-jp-3 models across different parameter sizes, please refer to:
  - [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa)
  - [LLM-jp-3 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-3-fine-tuned-models-672c621db852a01eae939731).


Checkpoints format: Hugging Face Transformers


## Required Libraries and Their Versions

- torch>=2.3.0
- transformers>=4.40.1
- tokenizers>=0.19.1
- accelerate>=0.29.3
- flash-attn>=2.5.8

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-8x1.8b-instruct3")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-8x1.8b-instruct3", device_map="auto", torch_dtype=torch.bfloat16)
chat = [
    {"role": "system", "content": "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。"},
    {"role": "user", "content": "自然言語処理とは何か"},
]
tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=100,
        do_sample=True,
        top_p=0.95,
        temperature=0.7,
        repetition_penalty=1.05,
    )[0]
print(tokenizer.decode(output))
```


## Model Details

- **Model type:** Transformer-based Language Model
- **Total seen tokens:** 2.1T tokens

|Params|Layers|Hidden size|Heads|Routed Experts|Activated Experts|Context length|Embedding parameters|Non-embedding parameters|Activated parameters|Total parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|8x1.8b|24|2048|16|8|2|4096|407,498,752|8,858,863,616|2,924,279,808|9,266,362,368|9,266,362,368|
|8x13b|40|5120|40|8|2|4096|1,018,746,880|72,144,081,920|22,200,806,400|73,162,828,800|

If you would like to learn more about the pretraining of the LLM-jp-3 MoE series, please refer to this [blog post](https://llm-jp.nii.ac.jp/blog/2025/03/27/moe3.html).

## Tokenizer

The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
The vocabulary entries were converted from [`llm-jp-tokenizer v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).

## Datasets

### Pre-training

The models have been pre-trained using a blend of the following datasets.

| Language | Dataset | Tokens|
|:---|:---|---:|
|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B

### Post-training

We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.

#### Supervised Fine-tuning
The datasets used for supervised fine-tuning are as follows:
 
| Language | Dataset | Description |
|:---|:---|:---|
|Japanese|[ichikara-instruction-004-002](https://liat-aip.sakura.ne.jp/wp/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf%e4%bd%9c%e6%88%90/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf-%e5%85%ac%e9%96%8b/)| A manually constructed instruction dataset. |
|        |[AnswerCarefully (ver2.0)](https://huggingface.co/datasets/llm-jp/AnswerCarefully)| A manually constructed instruction dataset focusing on LLMs' safety. |
|        |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited with some constraints on the output format. | 
|        |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
|        |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
|        |[wizardlm8x22b-logical-math-coding-sft-ja](https://huggingface.co/datasets/llm-jp/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. |
|        |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - | 
|        |[FLAN](https://huggingface.co/datasets/llm-jp/FLAN/blob/main/README.md) | - |
|Japanese & English|[Synthetic-JP-EN-Coding-Dataset](https://huggingface.co/datasets/llm-jp/Synthetic-JP-EN-Coding-Dataset)| A synthetic instruction dataset. |


#### Direct Preference Optimization

The datasets used for supervised fine-tuning are as follows:

| Language | Dataset | Description |
|:---|:---|:---|
|Japanese|[aya-ja-evol-inst](https://huggingface.co/datasets/llm-jp/aya-ja-evol-inst) | A synthetic preference dataset focusing on LLMs' helpfulness. |
|        |[ac-self-inst](https://huggingface.co/datasets/llm-jp/ac-self-inst)| A synthetic preference dataset focusing on LLMs' safety. |


## Evaluation

### llm-jp-eval (v1.4.1)

We evaluated the models using 100 examples from the dev split. Note that we skipped the CG (Code Generation) task.

| Model name | average | EL | FA | HE | MC | MR | MT | NLI | QA | RC | SUM |
| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| [llm-jp/llm-jp-3-7.2b](https://huggingface.co/llm-jp/llm-jp-3-7.2b) | 0.455 | 0.400 | 0.266 | 0.350 | 0.547 | 0.430 | 0.809 | 0.362 | 0.545 | 0.814 | 0.028 |
| [llm-jp/llm-jp-3-7.2b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct3) | 0.514 | 0.447 | 0.245 | 0.435 | 0.693 | 0.510 | 0.826 | 0.588 | 0.497 | 0.838 | 0.059 |
| [llm-jp/llm-jp-3-172b](https://huggingface.co/llm-jp/llm-jp-3-172b) | 0.543 | 0.408 | 0.266 | 0.515 | 0.763 | 0.670 | 0.823 | 0.574 | 0.569 | 0.829 | 0.015 |
| [llm-jp/llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 0.613 | 0.517 | 0.271 | 0.570 | 0.873 | 0.730 | 0.844 | 0.728 | 0.601 | 0.883 | 0.112 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [llm-jp/llm-jp-3-8x1.8b](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b) | 0.454 | 0.387 | 0.241 | 0.265 | 0.530 | 0.510 | 0.810 | 0.476 | 0.537 | 0.755 | 0.026 |
| [llm-jp/llm-jp-3-8x1.8b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct2) | 0.513 | 0.448 | 0.230 | 0.405 | 0.643 | 0.560 | 0.815 | 0.566 | 0.561 | 0.837 | 0.066 |
| [llm-jp/llm-jp-3-8x1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct3) | 0.515 | 0.452 | 0.227 | 0.425 | 0.683 | 0.540 | 0.821 | 0.558 | 0.545 | 0.819 | 0.075 |
| [llm-jp/llm-jp-3-8x13b](https://huggingface.co/llm-jp/llm-jp-3-8x13b) | 0.587 | 0.545 | 0.291 | 0.495 | 0.803 | 0.720 | 0.838 | 0.578 | 0.646 | 0.854 | 0.097 |
| [llm-jp/llm-jp-3-8x13b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct2) | 0.626 | 0.552 | 0.289 | 0.525 | 0.897 | 0.750 | 0.836 | 0.682 | 0.637 | 0.907 | 0.182 | 
| [llm-jp/llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 0.625 | 0.548 | 0.285 | 0.525 | 0.907 | 0.760 | 0.839 | 0.688 | 0.627 | 0.904 | 0.164 |

### Japanese MT Bench

We evaluated the models using `gpt-4o-2024-08-06`.
The scores represent the average values obtained from five rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge).

| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| [llm-jp/llm-jp-3-7.2b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct3) | 5.79 | 3.46 | 5.94 | 8.15 | 3.95 | 4.46 | 7.51 | 6.23 | 6.66 |
| [llm-jp/llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 6.36 | 4.24 | 6.66 | 8.11 | 4.58 | 5.74 | 7.44 | 6.76 | 7.36 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [llm-jp/llm-jp-3-8x1.8b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct2) | 5.47 | 3.47 | 4.90 | 7.78 | 3.51 | 4.38 | 6.84 | 6.35 | 6.54 |
| [llm-jp/llm-jp-3-8x1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct3) | 5.52 | 3.60 | 5.23 | 7.81 | 3.87 | 4.53 | 6.40 | 5.98 | 6.72 |
| [llm-jp/llm-jp-3-8x13b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct2) | 6.62 | 4.50 | 6.53 | 8.56 | 5.30 | 6.03 | 7.86 | 7.10 | 7.12 |
| [llm-jp/llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 6.58 | 4.90 | 6.41 | 8.32 | 5.37 | 5.20 | 7.75 | 7.24 | 7.48 |


### AnswerCarefully-Eval

[AnswerCarefully-Eval](https://www.anlp.jp/proceedings/annual_meeting/2025/pdf_dir/Q4-19.pdf) assesses the safety of Japanese language model outputs using the LLM-as-a-Judge approach, based on the test set from [llm-jp/AnswerCarefully](https://huggingface.co/datasets/llm-jp/AnswerCarefully).
We evaluated the models using `gpt-4-0613`.
The scores represent the average values obtained from five rounds of inference and evaluation.


| Model name | Acceptance rate (%, ↑) | Violation rate (%, ↓) | 
| :--- | ---: | ---: |
| [llm-jp/llm-jp-3-7.2b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct3) | 92.86 | 2.44 |
| [llm-jp/llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 95.48 | 1.67 |
| --- | --- | --- |
| [llm-jp/llm-jp-3-8x1.8b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct2) | 86.13 | 7.56 |
| [llm-jp/llm-jp-3-8x1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct3) | 92.20 | 2.20 |
| [llm-jp/llm-jp-3-8x13b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct2) | 88.63 | 6.01 |
| [llm-jp/llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 94.35 | 1.55 |



## Risks and Limitations

The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.


## Send Questions to

llm-jp(at)nii.ac.jp


## License

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## How to cite

If you find our work helpful, please feel free to cite the paper.

```
@inproceedings{
    nakamura2025dropupcycling,
    title={Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization},
    author={Taishi Nakamura and Takuya Akiba and Kazuki Fujii and Yusuke Oda and Rio Yokota and Jun Suzuki},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={https://openreview.net/forum?id=gx1wHnf5Vp}
}
```

## Model Card Authors

*The names are listed in alphabetical order.*

Hirokazu Kiyomaru, Takashi Kodama and Taishi Nakamura.