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Model: llm-jp/llm-jp-3.1-1.8b Source: Original Platform
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---
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license: apache-2.0
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language:
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- en
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- ja
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programming_language:
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- C
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- C++
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- C#
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- Go
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- Java
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- JavaScript
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- Lua
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- PHP
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- Python
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- Ruby
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- Rust
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- Scala
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- TypeScript
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pipeline_tag: text-generation
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library_name: transformers
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inference: false
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---
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# llm-jp-3.1-1.8b
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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/).
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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.
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This repository provides the **llm-jp-3.1-1.8b** model.
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For an overview of the LLM-jp-3.1 models across different parameter sizes, please refer to:
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- [LLM-jp-3.1 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-31-pre-trained-models-68368787c32e462c40a45f7b)
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- [LLM-jp-3.1 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-31-fine-tuned-models-68368681b9b35de1c4ac8de4).
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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).
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Checkpoints format: Hugging Face Transformers
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## Required Libraries and Their Versions
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- torch>=2.3.0
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- transformers>=4.40.1
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- tokenizers>=0.19.1
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- accelerate>=0.29.3
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- flash-attn>=2.5.8
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3.1-1.8b")
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3.1-1.8b", device_map="auto", torch_dtype=torch.bfloat16)
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text = "自然言語処理とは何か"
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tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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tokenized_input,
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max_new_tokens=100,
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do_sample=True,
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top_p=0.95,
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temperature=0.7,
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repetition_penalty=1.05,
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)[0]
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print(tokenizer.decode(output))
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```
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## Model Details
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- **Model type:** Transformer-based Language Model
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- **Architectures:**
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Dense model:
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|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
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|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
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|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
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MoE model:
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|Params|Layers|Hidden size|Heads|Routed Experts|Activated Experts|Context length|Embedding parameters|Non-embedding parameters|Activated parameters|Total parameters|
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|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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|8x13b|40|5120|40|8|2|4096|1,018,746,880|72,144,081,920|22,200,806,400|73,162,828,800|
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## Tokenizer
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The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
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The vocabulary entries were converted from [`llm-jp-tokenizer v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
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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).
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## Datasets
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### Pre-training
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The models have been pre-trained using a blend of the following datasets.
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| Language | Dataset | Tokens|
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|:---|:---|---:|
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|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
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||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
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||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
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||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
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||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
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|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
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||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
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||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
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||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
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||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
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||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
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||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
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|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
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|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
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|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B
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### Mid-training
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In the LLM-jp-3.1 series, we performed continuous pre-training based on [Instruction Pre-Training](https://aclanthology.org/2024.emnlp-main.148/).
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Instruction Pre-Training enhances a model’s ability to follow instructions by continuing pre-training on a large collection of instruction–response pairs.
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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.
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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.
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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.
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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.
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### Post-training
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We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.
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#### Supervised Fine-tuning
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The datasets used for supervised fine-tuning are as follows:
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| Language | Dataset | Description |
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|:---|:---|:---|
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|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. |
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| |[AnswerCarefully (ver2.0)](https://huggingface.co/datasets/llm-jp/AnswerCarefully)| A manually constructed instruction dataset focusing on LLMs' safety. |
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| |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited with some constraints on the output format. |
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| |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
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| |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
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| |[wizardlm8x22b-logical-math-coding-sft-ja](https://huggingface.co/datasets/llm-jp/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. |
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| |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
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| |[jaster v1.4.1](https://github.com/llm-jp/llm-jp-eval/tree/v1.4.1)| - |
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| |[extraction-wiki-ja](https://huggingface.co/datasets/llm-jp/extraction-wiki-ja)| A synthetic instruction dataset we created. |
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|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
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|Japanese & English|[Synthetic-JP-EN-Coding-Dataset](https://huggingface.co/datasets/llm-jp/Synthetic-JP-EN-Coding-Dataset)| A synthetic instruction dataset. |
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#### Direct Preference Optimization
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For Direct Preference Optimization (DPO), we adopted rejection sampling.
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Prompts were sampled from the dataset used in SFT, and multiple responses were generated for each prompt.
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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.
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We conducted DPO in two stages.
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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.
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## Evaluation
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### MT Bench (Japanese and English)
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We evaluated the models using `gpt-4o-2024-08-06`.
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The scores represent the average values obtained from three rounds of inference and evaluation.
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For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
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| Model Name | JA | EN |
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|:------------------------------------------------------------------------------------------------------------------------------|----------:|-------:|
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| gpt-35-turbo-1106 | 6.48 | 7.56 |
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| gpt-4-0613 | 7.29 | 7.72 |
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| gpt-4o-2024-08-06 | 8.10 | 8.38 |
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| [sbintuitions/sarashina2.2-1b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-1b-instruct-v0.1) | 5.30 | 5.66 |
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| [sbintuitions/sarashina2.2-3b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-3b-instruct-v0.1) | 7.07 | 6.96 |
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| [Rakuten/RakutenAI-2.0-8x7B-instruct](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B-instruct) | 6.68 | 6.33 |
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| [cyberagent/calm3-22b-chat](https://huggingface.co/cyberagent/calm3-22b-chat) | 6.86 | 6.77 |
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| [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | 7.07 | 7.99 |
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| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 7.64 | 8.27 |
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| [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | 5.46 | 6.95 |
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| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | 8.00 | 8.30 |
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| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | 8.36 | 8.33 |
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| [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 |
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| [stockmark/Stockmark-2-100B-Instruct-beta](https://huggingface.co/stockmark/Stockmark-2-100B-Instruct-beta) | 7.42 | 7.17 |
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| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.64 | 4.09 |
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| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 6.21 | 6.13 |
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| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 6.60 | 6.49 |
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| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 6.30 | 5.70 |
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| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 7.37 | 7.01 |
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| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 7.50 | 7.05 |
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### AnswerCarefully-Eval
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[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).
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We evaluated the models using `gpt-4o-2024-08-06`.
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The scores represent the average values obtained from three rounds of inference and evaluation.
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For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
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| Model name | Score | Acceptance rate (%, ↑) | Violation rate (%, ↓) |
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| :--- | ---: | ---: | ---: |
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| gpt-35-turbo-1106 | 3.98 | 71.7 | 12.6 |
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| gpt-4-0613 | 4.06 | 72.3 | 13.2 |
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| gpt-4o-2024-08-06 | 4.09 | 72.7 | 12.5 |
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| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.03 | 75.9 | 12.2 |
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| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 4.37 | 88.4 | 6.5 |
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| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 4.48 | 91.6 | 4.3 |
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| [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 |
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| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 4.17 | 82.4 | 12.2 |
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| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 4.26 | 83.1 | 11.6 |
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||||
|
||||
|
||||
|
||||
## 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.
|
||||
30
config.json
Normal file
30
config.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"_name_or_path": "llm-jp/llm-jp-3-1.8b",
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 7168,
|
||||
"max_position_embeddings": 4096,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 24,
|
||||
"num_key_value_heads": 16,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.49.0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 99584
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
3
model-00001-of-00001.safetensors
Normal file
3
model-00001-of-00001.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c734639f810581addc943c0c26d0b1ff1991bbe22adf96eaed86fa3f391d185c
|
||||
size 3735253776
|
||||
1
model.safetensors.index.json
Normal file
1
model.safetensors.index.json
Normal file
File diff suppressed because one or more lines are too long
10
special_tokens_map.json
Normal file
10
special_tokens_map.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"bos_token": "<s>",
|
||||
"cls_token": "<CLS|LLM-jp>",
|
||||
"eod_token": "</s>",
|
||||
"eos_token": "</s>",
|
||||
"mask_token": "<MASK|LLM-jp>",
|
||||
"pad_token": "<PAD|LLM-jp>",
|
||||
"sep_token": "<SEP|LLM-jp>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:955dc1fa623fab38cc92a3f4ee172423ae6d73201c4207569bfdf5626bc733f0
|
||||
size 6416433
|
||||
18
tokenizer_config.json
Normal file
18
tokenizer_config.json
Normal file
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"unk_token": "<unk>",
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"pad_token": "<PAD|LLM-jp>",
|
||||
"cls_token": "<CLS|LLM-jp>",
|
||||
"sep_token": "<SEP|LLM-jp>",
|
||||
"eod_token": "</s>",
|
||||
"mask_token": "<MASK|LLM-jp>",
|
||||
"extra_ids": 0,
|
||||
"sp_model_kwargs": {},
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"special_tokens_map_file": null,
|
||||
"tokenizer_class": "PreTrainedTokenizerFast"
|
||||
}
|
||||
Reference in New Issue
Block a user