---
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-3-3.7b-instruct

This repository provides 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/).


| Model Variants | 
| :--- |
| [llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b) |
| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) |
| [llm-jp-3-3.7b](https://huggingface.co/llm-jp/llm-jp-3-3.7b) |
| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) |
| [llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) |
| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) |
| [llm-jp-3-172b-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1) |
| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) |


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-3-3.7b-instruct")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-3.7b-instruct", 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

|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|1.8b|24|2048|16|4096|407,896,064|1,459,718,144|
|3.7b|28|3072|24|4096|611,844,096|3,171,068,928|
|13b|40|5120|40|4096|1,019,740,160|12,688,184,320|

## 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

### Instruction tuning

The models have been fine-tuned on the following datasets.
 
| 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 |
|        |[answer-carefully-002](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/)| A manually constructed instruction dataset focusing on LLMs' safety |
|        |ichikara-instruction-format| A small amount of instruction dataset edited from ichikara-instruction, 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_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)| A synthetic instruction dataset. |
|        |[Synthetic-JP-EN-Coding-Dataset-567k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-567k)| A synthetic instruction dataset. We used sampled one.|
|English |[FLAN](https://huggingface.co/datasets/Open-Orca/FLAN) | We used sampled one. | 


## Evaluation

### llm-jp-eval (v1.3.1)

We evaluated the models using 100 examples from the dev split.

| Model name | average | EL | FA | HE | MC | MR | MT | NLI | QA | RC |
| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| [llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b) | 0.3767 | 0.3725 | 0.1948 | 0.2350 | 0.2500 | 0.0900 | 0.7730 | 0.3080 | 0.4629 | 0.7040 |
| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 0.4596 | 0.4280 | 0.1987 | 0.3250 | 0.3300 | 0.4200 | 0.7900 | 0.3520 | 0.4698 | 0.8224 |
| [llm-jp-3-3.7b](https://huggingface.co/llm-jp/llm-jp-3-3.7b) | 0.4231 | 0.3812 | 0.2440 | 0.2200 | 0.1900 | 0.3600 | 0.7947 | 0.3800 | 0.4688 | 0.7694 |
| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) | 0.5188 | 0.4191 | 0.2504 | 0.3400 | 0.5000 | 0.5800 | 0.8166 | 0.4500 | 0.4881 | 0.8247 |
| [llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) | 0.5802 | 0.5570 | 0.2593 | 0.4600 | 0.7000 | 0.6300 | 0.8292 | 0.3460 | 0.5937 | 0.8469 |
| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) | 0.6168 | 0.5408 | 0.2757 | 0.4950 | 0.9200 | 0.7100 | 0.8317 | 0.4640 | 0.4642 | 0.8500 |


### Japanese MT Bench

We evaluated the models using `gpt-4-0613`. Please see the [codes](https://github.com/llm-jp/llm-leaderboard/tree/main) for details.

| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 4.93 | 1.50 | 4.70 | 7.80 | 1.55 | 2.60 | 7.80 | 6.10 | 7.40 |
| [llm-jp-3-3.7b-instruct](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) | 5.50 | 1.95 | 4.05 | 8.25 | 2.25 | 4.00 | 8.80 | 7.25 | 7.45 |
| [llm-jp-3-13b-instruct](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct) | 6.47 | 3.15 | 7.05 | 9.15 | 3.75 | 5.40 | 8.30 | 7.50 | 7.45 |



## 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)


## Model Card Authors

*The names are listed in alphabetical order.*

Hirokazu Kiyomaru and Takashi Kodama.