--- thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png license: llama3 datasets: - CohereForAI/aya_dataset - kunishou/databricks-dolly-15k-ja - kunishou/HelpSteer-35k-ja - kunishou/HelpSteer2-20k-ja - kunishou/hh-rlhf-49k-ja - kunishou/oasst1-chat-44k-ja - kunishou/oasst2-chat-68k-ja - meta-math/MetaMathQA - OpenAssistant/oasst1 - OpenAssistant/oasst2 - sahil2801/CodeAlpaca-20k language: - ja - en tags: - llama - llama-3 inference: false base_model: - rinna/llama-3-youko-8b - meta-llama/Meta-Llama-3-8B - meta-llama/Meta-Llama-3-8B-Instruct base_model_relation: merge --- # `Llama 3 Youko 8B Instruct (rinna/llama-3-youko-8b-instruct)` ![rinna-icon](./rinna.png) # Overview 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. | Size | Continual Pre-Training | Instruction-Tuning | | :- | :- | :- | | 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) | | 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) | * **Model architecture** 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. * **Training: Built with Meta Llama 3** **Supervised fine-tuning.** The supervised fine-tuning data is a subset of the following datasets. - [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) - The JPN subset was used. - [FLAN](https://github.com/google-research/FLAN/tree/main/flan/v2) - [kunishou/databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [kunishou/hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) - [kunishou/oasst1-chat-44k-ja](https://huggingface.co/datasets/kunishou/oasst1-chat-44k-ja) - [kunishou/oasst2-chat-68k-ja](https://huggingface.co/datasets/kunishou/oasst2-chat-68k-ja) - [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) - The following sections were used: MATH_AnsAug, MATH_Rephrased, MATH_SV, and MATH_FOBAR. - The remaining sections, containing augmented data from commonly used evaluation corpora, were skipped for preventing any possibility of data leak. - [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) - The EN and JA subsets were used. - [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) - The EN and JA subsets were used. - [sahil2801/CodeAlpaca-20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) - rinna Dataset **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). ~~~~text llama-3-youko-8b-sft + 0.5 * (meta-llama/Meta-Llama-3-8B-Instruct - meta-llama/Meta-Llama-3-8B) ~~~~ Here, the embedding layer was skipped while subtracting and adding the parameter vectors. **Direct preference optimization** was then applied with a subset of the following datasets to build this instruct model. - [kunishou/HelpSteer-35k-ja](https://huggingface.co/datasets/kunishou/HelpSteer-35k-ja) - [kunishou/HelpSteer2-20k-ja](https://huggingface.co/datasets/kunishou/HelpSteer2-20k-ja) - rinna Dataset * **Contributors** - [Xinqi Chen](https://huggingface.co/Keely0419) - [Koh Mitsuda](https://huggingface.co/mitsu-koh) - [Toshiaki Wakatsuki](https://huggingface.co/t-w) - [Kei Sawada](https://huggingface.co/keisawada) --- # Benchmarking Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html). --- # How to use the model 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. ~~~~python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "rinna/llama-3-youko-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"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。"}, {"role": "user", "content": "西田幾多郎とはどんな人物ですか？"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.convert_tokens_to_ids("<|end_of_text|>"), tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = model.generate( input_ids, max_new_tokens=512, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, repetition_penalty=1.1, ) response = outputs[0][input_ids.shape[-1]:] response = tokenizer.decode(response, skip_special_tokens=True) print(response) ~~~~ --- # Tokenization The model uses the original [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) tokenizer. --- # How to cite ```bibtex @misc{rinna-llama-3-youko-8b-instruct, title = {rinna/llama-3-youko-8b-instruct}, author = {Chen, Xinqi and Mitsuda, Koh and Wakatsuki, Toshiaki and Sawada, Kei}, url = {https://huggingface.co/rinna/llama-3-youko-8b-instruct} } @inproceedings{sawada2024release, title = {Release of Pre-Trained Models for the {J}apanese Language}, author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh}, booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, month = {5}, year = {2024}, pages = {13898--13905}, url = {https://aclanthology.org/2024.lrec-main.1213}, note = {\url{https://arxiv.org/abs/2404.01657}} } ``` --- # References ```bibtex @article{llama3modelcard, title = {Llama 3 Model Card}, author = {AI@Meta}, year = {2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } @article{huang2023chat, title = {Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages}, 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}, year = {2023}, url = {https://arxiv.org/abs/2310.04799} } ``` --- # License [Meta Llama 3 Community License](https://llama.meta.com/llama3/license/)