README.md

---
license: other
license_name: llm-jp-3-172b-instruct3-tou
license_link: https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3/raw/main/LICENSE_ja
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-3-172b-instruct3

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/).

The development was partially supported by [GENIAC](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html).

| 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) |
| [llm-jp-3-172b-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2) |
| [llm-jp-3-172b-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2) |
| [llm-jp-3-172b](https://huggingface.co/llm-jp/llm-jp-3-172b) |
| [llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) |


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-3-172b-instruct3")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-172b-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:**:
  - llm-jp-3-1.8b: 2.1T
  - llm-jp-3-3.7b: 2.1T
  - llm-jp-3-13b: 2.1T
  - llm-jp-3-172b-beta1: 0.7T
  - llm-jp-3-172b-beta2: 1.4T
  - llm-jp-3-172b: 2.1T

|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
|3.7b|28|3072|24|4096|611,248,128|3,171,068,928|
|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
|172b|96|12288|96|4096|2,444,992,512|169,947,181,056|


## 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 Japanese 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 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/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. We used a sampled subset. |
|        |[wizardlm8x22b-logical-math-coding-sft_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)| A synthetic instruction dataset. We used a sampled subset. |
|        |[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/Open-Orca/FLAN) | We used a sampled subset. |
|Japanese & English|[Synthetic-JP-EN-Coding-Dataset-567k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-567k)| A synthetic instruction dataset. We used a sampled subset. |


#### Direct Preference Optimization

We used synthetic preference data to improve both the helpfulness and harmlessness of the LLM. The datasets will be made available soon.

## 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-3-172b-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1) | 0.5174 | 0.4460 | 0.2556 | 0.3700 | 0.6400 | 0.6100 | 0.8265 | 0.5600 | 0.5720 | 0.8505 | 0.0434 |
| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) | 0.5700 | 0.4306 | 0.2292 | 0.4350 | 0.8433 | 0.6200 | 0.8228 | 0.6820 | 0.5873 | 0.8964 | 0.1529 |
| [llm-jp-3-172b-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2) | 0.5422 | 0.3337 | 0.2725 | 0.4700 | 0.7767 | 0.6900 | 0.8283 | 0.5960 | 0.6133 | 0.8380 | 0.0037 |
| [llm-jp-3-172b-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2) | 0.6022 | 0.5470 | 0.2665 | 0.5100 | 0.8600 | 0.7000 | 0.8392 | 0.6800 | 0.6346 | 0.8770 | 0.1076 |
| [llm-jp-3-172b](https://huggingface.co/llm-jp/llm-jp-3-172b) | 0.5431 | 0.4077 | 0.2662 | 0.5150 | 0.7633 | 0.6700 | 0.8227 | 0.5740 | 0.5686 | 0.8289 | 0.0148 |
| [llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 0.6130 | 0.5173 | 0.2711 | 0.5700 | 0.8733 | 0.7300 | 0.8437 | 0.7280 | 0.6012 | 0.8829 | 0.1121 |



### Japanese MT Bench

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

| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| [llm-jp-3-172b-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct) | 5.14 | 2.90 | 5.30 | 8.80 | 2.15 | 2.45 | 6.95 | 7.45 | 5.15 | 
| [llm-jp-3-172b-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2) | 6.72 | 4.10 | 6.90 | 7.60 | 4.00 | 6.35 | 8.70 | 7.95 | 8.15 |
| [llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-beta2-instruct3) | 7.57 | 4.85 | 8.55 | 9.56 | 3.75 | 7.6 | 8.1 | 8.95 | 9.2 |


## 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

See the [LICENSE](LICENSE_ja) file.

## Model Card Authors

*The names are listed in alphabetical order.*

Hirokazu Kiyomaru and Takashi Kodama.