rinna
/

llama-3-youko-8b-instruct

@@ -1,187 +1,188 @@
----
-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
----
-# `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/)

+---
+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
+---
+# `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/)