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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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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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base_model: |
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- rinna/llama-3-youko-70b |
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- meta-llama/Meta-Llama-3-70B |
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- meta-llama/Meta-Llama-3-70B-Instruct |
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base_model_relation: merge |
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--- |
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# `Llama 3 Youko 70B Instruct (rinna/llama-3-youko-70b-instruct)` |
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# Overview |
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The model is the instruction-tuned version of [rinna/llama-3-youko-70b](https://huggingface.co./rinna/llama-3-youko-70b), using supervised fine-tuning (SFT) and [Chat Vector](https://arxiv.org/abs/2310.04799). 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 80-layer, 8192-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 the following dataset. |
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- rinna Dataset |
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**Model merging.** The fine-tuned model (llama-3-youko-70b-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-3-70B](https://huggingface.co./meta-llama/Meta-Llama-3-70B) from those of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3-70B-Instruct). |
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~~~~text |
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llama-3-youko-70b-sft + 0.5 * (meta-llama/Meta-Llama-3-70B-Instruct - meta-llama/Meta-Llama-3-70B) |
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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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* **Contributors** |
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- [Koh Mitsuda](https://huggingface.co./mitsu-koh) |
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- [Xinqi Chen](https://huggingface.co./Keely0419) |
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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-70b-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-3-70B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3-70B-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-70b-instruct, |
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title = {rinna/llama-3-youko-70b-instruct}, |
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author = {Mitsuda, Koh and Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei}, |
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url = {https://huggingface.co./rinna/llama-3-youko-70b-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/) |
