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Rakuten/
RakutenAI-2.0-8x7B-instruct

Name: RakutenAI-2.0-8x7B-instruct
Availability: InStock

safetensors

mixtral

Rakuten/RakutenAI-2.0-8x7B

finetune

apache-2.0

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RakutenAI-2.0-8x7B-instruct

Model Description

RakutenAI-2.0-8x7B-instruct is a fine-tuned variant of RakutenAI-2.0-8x7B, designed to push the boundaries of Japanese large language models (LLMs). Developed as part of Rakuten's systematic effort to refine AI capabilities, this model builds upon the strengths of its foundation counterpart, excelling in instruction-following tasks while maintaining fluency, coherence, and contextual awareness.

Within the competitive 13B active parameter category, RakutenAI-2.0-8x7B-instruct achieves one of the highest scores on the Japanese MT-Bench.

If you are looking for foundation model, check RakutenAI-2.0-8x7B.

Model Evaluation Results

Instruct Model Name	Size	Active Parameters	Japanese MT-Bench Score
llm-jp/llm-jp-3-13b-instruct	13B	13B	5.68
elyza/ELYZA-japanese-Llama-2-13b-instruct	13B	13B	4.09
Rakuten/RakutenAI-2.0-8x7B-instruct	8x7B (47B)	13B	7.08
weblab-GENIAC/Tanuki-8x8B-dpo-v1.0	8x8B (47B)	13B	6.96
karakuri-ai/karakuri-lm-8x7b-instruct-v0.1	8x7B (47B)	13B	5.92
cyberagent/calm3-22b-chat	22B	22B	6.93

Table1: RakutenAI-2.0-8x7B-instruct performance on MT Bench in comparison with other Japanese open models.

Note on Evaluation Scores:

Japanese MT-bench is a set of 80 challenging open-ended questions for evaluating chat assistants on eight dimensions: writing, roleplay, reasoning, math, coding, extraction, stem, humanities. https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge Evaluation of responses is conducted with GPT4(gpt-4o-2024-05-13) as a judge, in line with public leaderboard.
The Japanese research community cautions against not to evaluate fine-tuned models on LM Harness due to task contamination, so we have not included the LM-Harness scores in this model card for instruct models. LLM-jp: jasterを用いてインストラクションチューニングを施したモデルが、テストデータをインストラクションチューニングに使用していない場合でも, llm-jp-evalの評価スコアを非常に高くすることができることが明らかになっている. したがって、高い評価スコアを得たからといって、他のLLMよりも性能が優れていると断言するのは適切ではないことに注意されたい。 Machine Translation: It has become clear that models that have been instruction tuned using Jaster can achieve very high evaluation scores on LLM-JP-EVAL, even if test data is not used for instruction tuning. Therefore, please note that it is not appropriate to assert that a model's performance is superior to other LLMs just because it has a high evaluation score. More details can be found at llm-jp-eval.
Final score (7.08 +/- 0.035) for RakutenAI-2.0-8x7B-instruct is average of 3 runs on Japanese MT-Bench. Model outputs and judge outputs are uploaded for reference.

Model Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Rakuten/RakutenAI-2.0-8x7B-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
model.eval()

chat = [
    {"role": "system", "content": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."},
    {"role": "user", "content": "How to make an authentic Spanish Omelette?"},
]

input_ids = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device=model.device)
attention_mask = input_ids.ne(tokenizer.pad_token_id).long()
tokens = model.generate(
    input_ids,
    max_length=2048,
    do_sample=False,
    num_beams=1,
    pad_token_id=tokenizer.eos_token_id,
    attention_mask=attention_mask,
)
out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True)
print("ASSISTANT:\n" + out)
print()

Model Details

Developed by: Rakuten Group, Inc.
Language(s): Japanese, English
License: This model is licensed under Apache License, Version 2.0.
Model Architecture: Mixture of Experts (2 active experts)

Limitations and Bias

The suite of RakutenAI-2.0 models is capable of generating human-like text on a wide range of topics. However, like all LLMs, they have limitations and can produce biased, inaccurate, or unsafe outputs. Please exercise caution and judgement while interacting with them.

Citation

For citing our work on the suite of RakutenAI-2.0 models, please use:

@misc{rakutengroup2025rakutenai2.0,
  author = {Rakuten Group, Inc.},
  title = {RakutenAI-2.0},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Rakuten},
}