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Rakuten/RakutenAI-7B-chat-onnx
RakutenAI-7B-chat-onnx is a machine learning model from Rakuten. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
RakutenAI-7B-chat-onnx is an INT4-quantized, ONNX-exported chat model derived from RakutenAI-7B, optimized for efficient on-device inference across a range of hardware execution providers.
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From the Hugging Face model README
RakutenAI-7B-chat-onnx is an INT4-quantized, ONNX-exported chat model derived from RakutenAI-7B, optimized for efficient on-device inference across a range of hardware execution providers.
Starting from the RakutenAI-7B base model, the model was developed through the following stages:
The resulting chat model was then quantized to INT4 precision and exported to ONNX.
RakutenAI-7B uses the Mistral model architecture and is based on the Mistral-7B-v0.1 pre-trained checkpoint. Its vocabulary has been extended from 32k to 48k tokens to achieve a better character-per-token rate for Japanese.
The technical report for the initial RakutenAI-7B suite can be accessed at arXiv.
| Model Name | Avg. | JCS | JNLI | MARC-ja | JSQuAD | Jaqket v2 | XLSum-ja | xWino | MGSM |
|---|---|---|---|---|---|---|---|---|---|
| accuracy | accuracy | accuracy | exact-match | exact-match | rouge-2 | accuracy | accuracy | ||
| 3-shots | 3-shots | 3-shots | 2-shots | 1-shot | 1-shot | 0-shot | 5-shots | ||
| Rakuten/RakutenAI-7B-chat-onnx | 70.26 | 92.14 | 53.37 | 95.49 | 77.10 | 86.94 | 13.55 | 77.89 | 65.60 |
| Rakuten/RakutenAI-7B-chat | 58.80 | 79.27 | 81.64 | 72.34 | 60.63 | 69.24 | 12.02 | 71.22 | 24.00 |
| Model Name | Avg. | ARC | HellaSwag | MMLU | TruthfulQA |
|---|---|---|---|---|---|
| accuracy | accuracy | accuracy | accuracy | ||
| 25-shots | 10-shots | 5-shots | 6-shots | ||
| Rakuten/RakutenAI-7B-chat-onnx | 54.42 | 43.09 | 65.65 | 54.28 | 54.64 |
| Rakuten/RakutenAI-7B-chat | 59.20 | 50.00 | 75.94 | 54.51 | 56.34 |
To enable efficient on-device deployment, the chat model was quantized to INT4 and exported as separate ONNX builds targeting the following execution providers:
| Execution Provider | Target Hardware | Foundry Local Model ID |
|---|---|---|
| CPU EP | Generic CPU | rakutenai-7b-chat-generic-cpu:1 |
| GPU EP | Generic GPU | rakutenai-7b-chat-generic-gpu:1 |
| OpenVINO EP (GPU) | Intel GPU | rakutenai-7b-chat-openvino-gpu:1 |
| OpenVINO EP (NPU) | Intel NPU | rakutenai-7b-chat-openvino-npu:1 |
| VitisAI EP | AMD NPU | rakutenai-7b-chat-vitisai-npu:1 |
Each build is provided in its own branch.
These ONNX Runtime GenAI builds are compatible with Microsoft Foundry Local, Microsoft's on-device inferencing runtime. Foundry Local runs on ONNX Runtime and manages CPU, GPU, OpenVINO, and VitisAI execution providers automatically, so any of the five variants above can be loaded and served through Foundry Local's CLI, SDKs, or OpenAI-compatible local REST endpoint.
To use these builds, point Foundry Local at the repo folder directly:
# Point Foundry Local at the folder containing the EP variant subfolders
foundry cache cd ./RakutenAI-7B-chat-onnx
# Confirm Foundry Local recognizes the variants
foundry cache ls
# Load and chat interactively with a specific variant
foundry model run rakutenai-7b-chat-generic-gpu:1
Check Foundry Local docs for the version you're targeting.
The suite of RakutenAI-7B 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.
For citing our work on the suite of RakutenAI-7B models, please use:
@misc{rakutengroup2026rakutenai7bchatonnx,
title={RakutenAI-7B-chat-onnx},
year={2026},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/Rakuten/RakutenAI-7B-chat-onnx}}
}
@misc{rakutengroup2024rakutenai7b,
title={RakutenAI-7B: Extending Large Language Models for Japanese},
year={2024},
eprint={2403.15484},
archivePrefix={arXiv},
primaryClass={cs.CL}
}