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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+llama-3-youko-70b-instruct - GGUF
+- Model creator: https://huggingface.co/rinna/
+- Original model: https://huggingface.co/rinna/llama-3-youko-70b-instruct/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [llama-3-youko-70b-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.Q2_K.gguf) | Q2_K | 24.56GB |
+| [llama-3-youko-70b-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
+| [llama-3-youko-70b-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.IQ3_S.gguf) | IQ3_S | 28.79GB |
+| [llama-3-youko-70b-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
+| [llama-3-youko-70b-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.IQ3_M.gguf) | IQ3_M | 29.74GB |
+| [llama-3-youko-70b-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.Q3_K.gguf) | Q3_K | 31.91GB |
+| [llama-3-youko-70b-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
+| [llama-3-youko-70b-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
+| [llama-3-youko-70b-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
+| [llama-3-youko-70b-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/blob/main/llama-3-youko-70b-instruct.Q4_0.gguf) | Q4_0 | 37.22GB |
+| [llama-3-youko-70b-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | IQ4_NL | 37.58GB |
+| [llama-3-youko-70b-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q4_K_S | 37.58GB |
+| [llama-3-youko-70b-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q4_K | 39.6GB |
+| [llama-3-youko-70b-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q4_K_M | 39.6GB |
+| [llama-3-youko-70b-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q4_1 | 41.27GB |
+| [llama-3-youko-70b-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q5_0 | 45.32GB |
+| [llama-3-youko-70b-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q5_K_S | 45.32GB |
+| [llama-3-youko-70b-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q5_K | 46.52GB |
+| [llama-3-youko-70b-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q5_K_M | 46.52GB |
+| [llama-3-youko-70b-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q5_1 | 49.36GB |
+| [llama-3-youko-70b-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q6_K | 53.91GB |
+| [llama-3-youko-70b-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/rinna_-_llama-3-youko-70b-instruct-gguf/tree/main/) | Q8_0 | 69.83GB |
+
+
+
+
+Original model description:
+---
+thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
+license: llama3
+language:
+- ja
+- en
+tags:
+- llama
+- llama-3
+inference: false
+base_model: 
+- rinna/llama-3-youko-70b
+- meta-llama/Meta-Llama-3-70B
+- meta-llama/Meta-Llama-3-70B-Instruct
+base_model_relation: merge
+---
+
+# `Llama 3 Youko 70B Instruct (rinna/llama-3-youko-70b-instruct)`
+
+![rinna-icon](./rinna.png)
+
+# Overview
+
+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.
+
+
+| 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 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.
+
+* **Training: Built with Meta Llama 3**
+
+    **Supervised fine-tuning.** The supervised fine-tuning data is the following dataset.
+
+    - rinna Dataset
+
+    **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).
+
+    ~~~~text
+    llama-3-youko-70b-sft + 0.5 * (meta-llama/Meta-Llama-3-70B-Instruct - meta-llama/Meta-Llama-3-70B)
+    ~~~~
+
+    Here, the embedding layer was skipped while subtracting and adding the parameter vectors.
+
+* **Contributors**
+
+    - [Koh Mitsuda](https://huggingface.co/mitsu-koh)
+    - [Xinqi Chen](https://huggingface.co/Keely0419)
+    - [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-70b-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-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) tokenizer.
+
+---
+
+# How to cite
+```bibtex
+@misc{rinna-llama-3-youko-70b-instruct,
+    title = {rinna/llama-3-youko-70b-instruct},
+    author = {Mitsuda, Koh and Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei},
+    url = {https://huggingface.co/rinna/llama-3-youko-70b-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/)
+