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初始化项目，由ModelHub XC社区提供模型 Model: llm-jp/llm-jp-3.1-1.8b Source: Original Platform
2026-05-13 08:38:22 +08:00
---
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-3.1-1.8b
LLM-jp-3.1 is a 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/).
Building upon the LLM-jp-3 series, the LLM-jp-3.1 models incorporate mid-training ([instruction pre-training](https://aclanthology.org/2024.emnlp-main.148/)), which significantly enhances their instruction-following capabilities compared to the original LLM-jp-3 models.
This repository provides the **llm-jp-3.1-1.8b** model.
For an overview of the LLM-jp-3.1 models across different parameter sizes, please refer to:
- [LLM-jp-3.1 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-31-pre-trained-models-68368787c32e462c40a45f7b)
- [LLM-jp-3.1 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-31-fine-tuned-models-68368681b9b35de1c4ac8de4).
For more details on the training procedures and evaluation results, please refer to [this blog post](https://llm-jp.nii.ac.jp/ja/blog/blog-887/) (in Japanese).
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-3.1-1.8b")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3.1-1.8b", device_map="auto", torch_dtype=torch.bfloat16)
text = "自然言語処理とは何か"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, 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
- **Architectures:**
Dense model:
|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|
|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
MoE model:
|Params|Layers|Hidden size|Heads|Routed Experts|Activated Experts|Context length|Embedding parameters|Non-embedding parameters|Activated parameters|Total parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|8x13b|40|5120|40|8|2|4096|1,018,746,880|72,144,081,920|22,200,806,400|73,162,828,800|
## 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
### Mid-training
In the LLM-jp-3.1 series, we performed continuous pre-training based on [Instruction Pre-Training](https://aclanthology.org/2024.emnlp-main.148/).
Instruction Pre-Training enhances a model’s ability to follow instructions by continuing pre-training on a large collection of instruction–response pairs.
We prepared approximately 90B tokens of instruction–response data and mixed it with our pre-training datasets, conducting continuous pre-training on a total of 400B tokens.
Each model was initialized from existing checkpoints ([llm-jp/llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b), [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b), and [llm-jp/llm-jp-3-8x13b](https://huggingface.co/llm-jp/llm-jp-3-8x13b)) and underwent continuous instruction pre-training.
Since the LLM-jp-3 series was originally pre-trained on 2.1T tokens, the total pre-training token count amounts to 2.5T tokens.
Details of this training process will be released in a forthcoming paper. The instruction–response dataset used for this training will also be made publicly available.
### 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. |
| |[jaster v1.4.1](https://github.com/llm-jp/llm-jp-eval/tree/v1.4.1)| - |
| |[extraction-wiki-ja](https://huggingface.co/datasets/llm-jp/extraction-wiki-ja)| A synthetic instruction dataset we created. |
|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
|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
For Direct Preference Optimization (DPO), we adopted rejection sampling.
Prompts were sampled from the dataset used in SFT, and multiple responses were generated for each prompt.
These responses were then scored (by [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)), and DPO was performed by treating high-scoring responses as positive examples and low-scoring responses as negative examples.
We conducted DPO in two stages.
In the second stage, we additionally used [ac-self-inst](https://huggingface.co/datasets/llm-jp/ac-self-inst), a Japanese preference dataset focused on safety.
## Evaluation
### MT Bench (Japanese and English)
We evaluated the models using `gpt-4o-2024-08-06`.
The scores represent the average values obtained from three rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
| Model Name | JA | EN |
|:------------------------------------------------------------------------------------------------------------------------------|----------:|-------:|
| gpt-35-turbo-1106 | 6.48 | 7.56 |
| gpt-4-0613 | 7.29 | 7.72 |
| gpt-4o-2024-08-06 | 8.10 | 8.38 |
| [sbintuitions/sarashina2.2-1b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-1b-instruct-v0.1) | 5.30 | 5.66 |
| [sbintuitions/sarashina2.2-3b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-3b-instruct-v0.1) | 7.07 | 6.96 |
| [Rakuten/RakutenAI-2.0-8x7B-instruct](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B-instruct) | 6.68 | 6.33 |
| [cyberagent/calm3-22b-chat](https://huggingface.co/cyberagent/calm3-22b-chat) | 6.86 | 6.77 |
| [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | 7.07 | 7.99 |
| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 7.64 | 8.27 |
| [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | 5.46 | 6.95 |
| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | 8.00 | 8.30 |
| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | 8.36 | 8.33 |
| [tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) | 7.64 | 8.02 |
| [stockmark/Stockmark-2-100B-Instruct-beta](https://huggingface.co/stockmark/Stockmark-2-100B-Instruct-beta) | 7.42 | 7.17 |
| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.64 | 4.09 |
| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 6.21 | 6.13 |
| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 6.60 | 6.49 |
| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 6.30 | 5.70 |
| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 7.37 | 7.01 |
| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 7.50 | 7.05 |
### 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-4o-2024-08-06`.
The scores represent the average values obtained from three rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
| Model name | Score | Acceptance rate (%, ↑) | Violation rate (%, ↓) |
| :--- | ---: | ---: | ---: |
| gpt-35-turbo-1106 | 3.98 | 71.7 | 12.6 |
| gpt-4-0613 | 4.06 | 72.3 | 13.2 |
| gpt-4o-2024-08-06 | 4.09 | 72.7 | 12.5 |
| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.03 | 75.9 | 12.2 |
| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 4.37 | 88.4 | 6.5 |
| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 4.48 | 91.6 | 4.3 |
| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 3.66 | 64.7 | 24.3 |
| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 4.17 | 82.4 | 12.2 |
| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 4.26 | 83.1 | 11.6 |
## 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.
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