Create README.md

README.md

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+---
+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-instruct4
+
+LLM-jp-3.1 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/).
+
+The LLM-jp-3.1 series consists of models that have undergone mid-training ([instruction pre-training](https://aclanthology.org/2024.emnlp-main.148/)) based on the LLM-jp-3 series, resulting in a significant improvement in instruction-following capabilities compared to the original LLM-jp-3 models.
+
+
+This repository provides **llm-jp-3.1-1.8b-instruct4** 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).
+
+
+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-instruct4")
+model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3.1-1.8b-instruct4", 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
+
+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 continued pre-training based on [Instruction Pre-Training](https://aclanthology.org/2024.emnlp-main.148/).
+Instruction Pre-Training is a method that 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 continued 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 continued 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.
+
+In the case of *instruct4*, DPO was conducted 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.
+
+| 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 |    6.45 |
+| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3)     |   4.48 |    91.6 |    4.27 |
+| [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.