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Model: llm-jp/llm-jp-3-8x1.8b 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-3-8x1.8b
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LLM-jp-3 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/).
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This repository provides the **llm-jp-3-8x1.8b** model.
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For an overview of the LLM-jp-3 models across different parameter sizes, please refer to:
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- [LLM-jp-3 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-3-pre-trained-models-672c6096472b65839d76a1fa)
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- [LLM-jp-3 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-3-fine-tuned-models-672c621db852a01eae939731).
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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-3-8x1.8b")
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-8x1.8b", 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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- **Total seen tokens:** 2.1T tokens
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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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|8x1.8b|24|2048|16|8|2|4096|407,498,752|8,858,863,616|2,924,279,808|9,266,362,368|9,266,362,368|
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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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If you would like to learn more about the pretraining of the LLM-jp-3 MoE series, please refer to this [blog post](https://llm-jp.nii.ac.jp/blog/2025/03/27/moe3.html).
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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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### 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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|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
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| |[FLAN](https://huggingface.co/datasets/llm-jp/FLAN/blob/main/README.md) | - |
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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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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|[aya-ja-evol-inst](https://huggingface.co/datasets/llm-jp/aya-ja-evol-inst) | A synthetic preference dataset focusing on LLMs' helpfulness. |
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| |[ac-self-inst](https://huggingface.co/datasets/llm-jp/ac-self-inst)| A synthetic preference dataset focusing on LLMs' safety. |
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## Evaluation
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### llm-jp-eval (v1.4.1)
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We evaluated the models using 100 examples from the dev split. Note that we skipped the CG (Code Generation) task.
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| Model name | average | EL | FA | HE | MC | MR | MT | NLI | QA | RC | SUM |
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| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [llm-jp/llm-jp-3-7.2b](https://huggingface.co/llm-jp/llm-jp-3-7.2b) | 0.455 | 0.400 | 0.266 | 0.350 | 0.547 | 0.430 | 0.809 | 0.362 | 0.545 | 0.814 | 0.028 |
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| [llm-jp/llm-jp-3-7.2b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct3) | 0.514 | 0.447 | 0.245 | 0.435 | 0.693 | 0.510 | 0.826 | 0.588 | 0.497 | 0.838 | 0.059 |
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| [llm-jp/llm-jp-3-172b](https://huggingface.co/llm-jp/llm-jp-3-172b) | 0.543 | 0.408 | 0.266 | 0.515 | 0.763 | 0.670 | 0.823 | 0.574 | 0.569 | 0.829 | 0.015 |
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| [llm-jp/llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 0.613 | 0.517 | 0.271 | 0.570 | 0.873 | 0.730 | 0.844 | 0.728 | 0.601 | 0.883 | 0.112 |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| [llm-jp/llm-jp-3-8x1.8b](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b) | 0.454 | 0.387 | 0.241 | 0.265 | 0.530 | 0.510 | 0.810 | 0.476 | 0.537 | 0.755 | 0.026 |
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| [llm-jp/llm-jp-3-8x1.8b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct2) | 0.513 | 0.448 | 0.230 | 0.405 | 0.643 | 0.560 | 0.815 | 0.566 | 0.561 | 0.837 | 0.066 |
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| [llm-jp/llm-jp-3-8x1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct3) | 0.515 | 0.452 | 0.227 | 0.425 | 0.683 | 0.540 | 0.821 | 0.558 | 0.545 | 0.819 | 0.075 |
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| [llm-jp/llm-jp-3-8x13b](https://huggingface.co/llm-jp/llm-jp-3-8x13b) | 0.587 | 0.545 | 0.291 | 0.495 | 0.803 | 0.720 | 0.838 | 0.578 | 0.646 | 0.854 | 0.097 |
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| [llm-jp/llm-jp-3-8x13b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct2) | 0.626 | 0.552 | 0.289 | 0.525 | 0.897 | 0.750 | 0.836 | 0.682 | 0.637 | 0.907 | 0.182 |
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| [llm-jp/llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 0.625 | 0.548 | 0.285 | 0.525 | 0.907 | 0.760 | 0.839 | 0.688 | 0.627 | 0.904 | 0.164 |
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### Japanese MT Bench
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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 five 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).
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| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
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| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [llm-jp/llm-jp-3-7.2b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct3) | 5.79 | 3.46 | 5.94 | 8.15 | 3.95 | 4.46 | 7.51 | 6.23 | 6.66 |
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| [llm-jp/llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 6.36 | 4.24 | 6.66 | 8.11 | 4.58 | 5.74 | 7.44 | 6.76 | 7.36 |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| [llm-jp/llm-jp-3-8x1.8b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct2) | 5.47 | 3.47 | 4.90 | 7.78 | 3.51 | 4.38 | 6.84 | 6.35 | 6.54 |
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| [llm-jp/llm-jp-3-8x1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct3) | 5.52 | 3.60 | 5.23 | 7.81 | 3.87 | 4.53 | 6.40 | 5.98 | 6.72 |
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| [llm-jp/llm-jp-3-8x13b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct2) | 6.62 | 4.50 | 6.53 | 8.56 | 5.30 | 6.03 | 7.86 | 7.10 | 7.12 |
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| [llm-jp/llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 6.58 | 4.90 | 6.41 | 8.32 | 5.37 | 5.20 | 7.75 | 7.24 | 7.48 |
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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-4-0613`.
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The scores represent the average values obtained from five rounds of inference and evaluation.
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| Model name | Acceptance rate (%, ↑) | Violation rate (%, ↓) |
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| :--- | ---: | ---: |
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| [llm-jp/llm-jp-3-7.2b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-7.2b-instruct3) | 92.86 | 2.44 |
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| [llm-jp/llm-jp-3-172b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-172b-instruct3) | 95.48 | 1.67 |
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| --- | --- | --- |
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| [llm-jp/llm-jp-3-8x1.8b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct2) | 86.13 | 7.56 |
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| [llm-jp/llm-jp-3-8x1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x1.8b-instruct3) | 92.20 | 2.20 |
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| [llm-jp/llm-jp-3-8x13b-instruct2](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct2) | 88.63 | 6.01 |
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| [llm-jp/llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 94.35 | 1.55 |
|
||||
|
||||
|
||||
|
||||
## 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)
|
||||
|
||||
## How to cite
|
||||
|
||||
If you find our work helpful, please feel free to cite the paper.
|
||||
|
||||
```
|
||||
@inproceedings{
|
||||
nakamura2025dropupcycling,
|
||||
title={Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization},
|
||||
author={Taishi Nakamura and Takuya Akiba and Kazuki Fujii and Yusuke Oda and Rio Yokota and Jun Suzuki},
|
||||
booktitle={The Thirteenth International Conference on Learning Representations},
|
||||
year={2025},
|
||||
url={https://openreview.net/forum?id=gx1wHnf5Vp}
|
||||
}
|
||||
```
|
||||
|
||||
## Model Card Authors
|
||||
|
||||
*The names are listed in alphabetical order.*
|
||||
|
||||
Hirokazu Kiyomaru, Takashi Kodama and Taishi Nakamura.
|
||||
32
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"MixtralForCausalLM"
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"vocab_size": 99584
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}
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10
special_tokens_map.json
Normal file
10
special_tokens_map.json
Normal file
@@ -0,0 +1,10 @@
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{
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}
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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18
tokenizer_config.json
Normal file
18
tokenizer_config.json
Normal file
@@ -0,0 +1,18 @@
|
||||
{
|
||||
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||||
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"tokenizer_class": "PreTrainedTokenizerFast"
|
||||
}
|
||||
Reference in New Issue
Block a user