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---
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thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
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license: llama3
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datasets:
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- CohereForAI/aya_dataset
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- kunishou/databricks-dolly-15k-ja
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- kunishou/HelpSteer-35k-ja
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- kunishou/HelpSteer2-20k-ja
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- kunishou/hh-rlhf-49k-ja
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- kunishou/oasst1-chat-44k-ja
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- kunishou/oasst2-chat-68k-ja
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- meta-math/MetaMathQA
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- OpenAssistant/oasst1
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- OpenAssistant/oasst2
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- sahil2801/CodeAlpaca-20k
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language:
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- ja
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- en
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tags:
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- llama
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- llama-3
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inference: false
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---
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# `Llama 3 Youko 8B Instruct (rinna/llama-3-youko-8b-instruct)`
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# Overview
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The model is the instruction-tuned version of [rinna/llama-3-youko-8b](https://huggingface.co/rinna/llama-3-youko-8b), using supervised fine-tuning (SFT), Chat Vector, and direct preference optimization (DPO). It adpots the Llama-3 chat format.
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| Size | Continual Pre-Training | Instruction-Tuning |
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| :- | :- | :- |
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| 8B | Llama 3 Youko 8B [[HF]](https://huggingface.co/rinna/llama-3-youko-8b) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-8b-gptq) | Llama 3 Youko 8B Instruct [[HF]](https://huggingface.co/rinna/llama-3-youko-8b-instruct) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-8b-instruct-gptq) |
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| 70B | Llama 3 Youko 70B [[HF]](https://huggingface.co/rinna/llama-3-youko-70b) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-70b-gptq) | Llama 3 Youko 70B Instruct [[HF]](https://huggingface.co/rinna/llama-3-youko-70b-instruct) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-70b-instruct-gptq) |
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* **Model architecture**
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A 32-layer, 4096-hidden-size transformer-based language model. Refer to the [Llama 3 Model Card](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for architecture details.
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* **Training: Built with Meta Llama 3**
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**Supervised fine-tuning.** The supervised fine-tuning data is a subset of the following datasets.
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- [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset)
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- The JPN subset was used.
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- [FLAN](https://github.com/google-research/FLAN/tree/main/flan/v2)
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- [kunishou/databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
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- [kunishou/hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)
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- [kunishou/oasst1-chat-44k-ja](https://huggingface.co/datasets/kunishou/oasst1-chat-44k-ja)
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- [kunishou/oasst2-chat-68k-ja](https://huggingface.co/datasets/kunishou/oasst2-chat-68k-ja)
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- [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
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- The following sections were used: MATH_AnsAug, MATH_Rephrased, MATH_SV, and MATH_FOBAR.
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- The remaining sections, containing augmented data from commonly used evaluation corpora, were skipped for preventing any possibility of data leak.
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- [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
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- The EN and JA subsets were used.
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- [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2)
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- The EN and JA subsets were used.
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- [sahil2801/CodeAlpaca-20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
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- rinna Dataset
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**Model merging.** The fine-tuned model (llama-3-youko-8b-sft) has been enhanced through the following chat vector addition. The chat vector was obtained by subtracting the parameter vectors of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) from those of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
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~~~~text
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llama-3-youko-8b-sft + 0.5 * (meta-llama/Meta-Llama-3-8B-Instruct - meta-llama/Meta-Llama-3-8B)
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~~~~
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Here, the embedding layer was skipped while subtracting and adding the parameter vectors.
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**Direct preference optimization** was then applied with a subset of the following datasets to build this instruct model.
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- [kunishou/HelpSteer-35k-ja](https://huggingface.co/datasets/kunishou/HelpSteer-35k-ja)
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- [kunishou/HelpSteer2-20k-ja](https://huggingface.co/datasets/kunishou/HelpSteer2-20k-ja)
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- rinna Dataset
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* **Contributors**
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- [Xinqi Chen](https://huggingface.co/Keely0419)
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- [Koh Mitsuda](https://huggingface.co/mitsu-koh)
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- [Toshiaki Wakatsuki](https://huggingface.co/t-w)
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- [Kei Sawada](https://huggingface.co/keisawada)
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---
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# Benchmarking
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Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
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---
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# How to use the model
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We found this instruction-tuned model tends to generate repeated text more often than its base counterpart, and thus we set repetition_penalty=1.1 for better generation performance. The same repetition penalty was applied to the instruction-tuned model in the aforementioned evaluation experiments.
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~~~~python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "rinna/llama-3-youko-8b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "あなたは誠実で優秀なアシスタントです。どうか、簡潔かつ正直に答えてください。"},
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{"role": "user", "content": "西田幾多郎とはどんな人物ですか？"},
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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terminators = [
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tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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repetition_penalty=1.1,
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)
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response = outputs[0][input_ids.shape[-1]:]
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response = tokenizer.decode(response, skip_special_tokens=True)
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print(response)
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~~~~
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---
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# Tokenization
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The model uses the original [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) tokenizer.
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---
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# How to cite
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```bibtex
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@misc{rinna-llama-3-youko-8b-instruct,
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title = {rinna/llama-3-youko-8b-instruct},
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author = {Chen, Xinqi and Mitsuda, Koh and Wakatsuki, Toshiaki and Sawada, Kei},
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url = {https://huggingface.co/rinna/llama-3-youko-8b-instruct}
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}
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@inproceedings{sawada2024release,
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title = {Release of Pre-Trained Models for the {J}apanese Language},
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author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
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booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
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month = {5},
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year = {2024},
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pages = {13898--13905},
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url = {https://aclanthology.org/2024.lrec-main.1213},
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note = {\url{https://arxiv.org/abs/2404.01657}}
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}
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```
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---
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# References
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```bibtex
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@article{llama3modelcard,
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title = {Llama 3 Model Card},
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author = {AI@Meta},
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year = {2024},
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url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
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}
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@article{huang2023chat,
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title = {Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages},
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author = {Huang, Shih-Cheng and Li, Pin-Zu and Hsu, Yu-Chi and Chen, Kuang-Ming and Lin, Yu Tung and Hsiao, Shih-Kai and Tzong-Han Tsai, Richard and Lee, Hung-yi},
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year = {2023},
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url = {https://arxiv.org/abs/2310.04799}
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}
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```
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---
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# License
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[Meta Llama 3 Community License](https://llama.meta.com/llama3/license/) |
