Swallow
Our Swallow model has undergone continual pre-training from the Llama 2 family, primarily with the addition of Japanese language data. The tuned versions use supervised fine-tuning (SFT). Links to other models can be found in the index.
Model Release Updates
We are excited to share the release schedule for our latest models:
- April 26, 2024: Released version 0.1 of our enhanced instruction-tuned models: Swallow-7b-instruct-v0.1, Swallow-13b-instruct-v0.1, and Swallow-70b-instruct-v0.1 as preview versions.
- March 2, 2024: Released the Swallow-7b-plus-hf, a model trained with approximately twice as many Japanese tokens as Swallow-7b-hf.
- February 4, 2024: Released the Swallow-13b-NVE-hf.
- January 26, 2024: Released the Swallow-7b-NVE-hf, Swallow-7b-NVE-instruct-hf, Swallow-70b-NVE-hf, and Swallow-70b-NVE-instruct-hf
- December 19, 2023: Released the Swallow-7b-hf, Swallow-7b-instruct-hf, Swallow-13b-hf, Swallow-13b-instruct-hf, Swallow-70b-hf, and Swallow-70b-instruct-hf.
Swallow Model Index
Model | Swallow-hf | Swallow-instruct-hf | Swallow-instruct-v0.1 |
---|---|---|---|
7B | Link | Link | Link |
7B-Plus | Link | N/A | N/A |
13B | Link | Link | Link |
70B | Link | Link | Link |
Swallow Model Index NVE (No Vocabulary Expansion)
This repository provides large language models developed by TokyoTech-LLM.
Model Details
- Model type: Please refer to LLaMA-2 technical report for details on the model architecture.
- Language(s): Japanese English
- Tokenizer: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process.
- Contact: swallow[at]nlp.c.titech.ac.jp
Instruct Model Performance
MT-Bench JA
Comparison to the past version
- NOTE that the models with the
v0.1
suffix are newer versions compared to their original counterparts with thehf
. - We report overall (i.e., average over scores of the first and second turns), first, and second turn scores.
Overall
Model | Average | Writing | Roleplay | Reasoning | Math | Coding | Extraction | STEM | Humanities |
---|---|---|---|---|---|---|---|---|---|
Swallow-7b-instruct-v0.1 | 0.3435 | 0.4450 | 0.4720 | 0.1853 | 0.1920 | 0.2204 | 0.3015 | 0.4594 | 0.4720 |
Swallow-7b-instruct-hf | 0.1833 | 0.2205 | 0.1975 | 0.1593 | 0.1045 | 0.1282 | 0.2672 | 0.1908 | 0.1980 |
Swallow-13b-instruct-v0.1 | 0.3669 | 0.4816 | 0.5562 | 0.2769 | 0.1020 | 0.1505 | 0.4179 | 0.4347 | 0.5150 |
Swallow-13b-instruct-hf | 0.2004 | 0.1932 | 0.2552 | 0.1507 | 0.1184 | 0.1285 | 0.2641 | 0.2434 | 0.2500 |
Swallow-70b-instruct-v0.1 | 0.4513 | 0.4822 | 0.5353 | 0.3497 | 0.3492 | 0.2668 | 0.5553 | 0.4955 | 0.5767 |
Swallow-70b-instruct-hf | 0.3259 | 0.2925 | 0.4283 | 0.3447 | 0.1562 | 0.1856 | 0.5634 | 0.3315 | 0.3071 |
First Turn
Model | Average | Writing | Roleplay | Reasoning | Math | Coding | Extraction | STEM | Humanities |
---|---|---|---|---|---|---|---|---|---|
Swallow-7b-instruct-v0.1 | 0.3829 | 0.4960 | 0.4800 | 0.2220 | 0.2820 | 0.2164 | 0.3220 | 0.5440 | 0.4980 |
Swallow-7b-instruct-hf | 0.2216 | 0.2830 | 0.2150 | 0.1590 | 0.1080 | 0.1470 | 0.3542 | 0.2450 | 0.2650 |
Swallow-13b-instruct-v0.1 | 0.3948 | 0.5400 | 0.5220 | 0.3020 | 0.1040 | 0.1760 | 0.5040 | 0.5180 | 0.4920 |
Swallow-13b-instruct-hf | 0.2304 | 0.2460 | 0.2640 | 0.1610 | 0.1360 | 0.1330 | 0.3070 | 0.3010 | 0.2950 |
Swallow-70b-instruct-v0.1 | 0.4849 | 0.5720 | 0.5020 | 0.4780 | 0.3680 | 0.2467 | 0.5400 | 0.5720 | 0.5960 |
Swallow-70b-instruct-hf | 0.3631 | 0.3420 | 0.4007 | 0.4220 | 0.1580 | 0.2044 | 0.6120 | 0.4280 | 0.3360 |
Second Turn
Model | Average | Writing | Roleplay | Reasoning | Math | Coding | Extraction | STEM | Humanities |
---|---|---|---|---|---|---|---|---|---|
Swallow-7b-instruct-v0.1 | 0.3059 | 0.3940 | 0.4640 | 0.1441 | 0.1000 | 0.2253 | 0.2811 | 0.3724 | 0.4449 |
Swallow-7b-instruct-hf | 0.1432 | 0.1567 | 0.1798 | 0.1603 | 0.1010 | 0.1085 | 0.1767 | 0.1343 | 0.1295 |
Swallow-13b-instruct-v0.1 | 0.3353 | 0.4213 | 0.5911 | 0.2516 | 0.1000 | 0.1244 | 0.3194 | 0.3473 | 0.5394 |
Swallow-13b-instruct-hf | 0.1692 | 0.1364 | 0.2453 | 0.1401 | 0.1000 | 0.1237 | 0.2199 | 0.1850 | 0.2050 |
Swallow-70b-instruct-v0.1 | 0.4179 | 0.3913 | 0.5689 | 0.2184 | 0.3280 | 0.2884 | 0.5711 | 0.4171 | 0.5562 |
Swallow-70b-instruct-hf | 0.2872 | 0.2398 | 0.4564 | 0.2647 | 0.1540 | 0.1676 | 0.5118 | 0.2311 | 0.2762 |
Comparison to the existing models
We only provide the overall score in this section.
7B models
Model | Average | Writing | Roleplay | Reasoning | Math | Coding | Extraction | STEM | Humanities |
---|---|---|---|---|---|---|---|---|---|
Swallow-7b-instruct-v0.1 | 0.3435 | 0.4450 | 0.4720 | 0.1853 | 0.1920 | 0.2204 | 0.3015 | 0.4594 | 0.4720 |
ELYZA-japanese-Llama-2-7b-fast-instruct | 0.2827 | 0.3289 | 0.3907 | 0.2424 | 0.1480 | 0.1584 | 0.3511 | 0.3053 | 0.3365 |
calm2-7b-chat | 0.3204 | 0.4657 | 0.4898 | 0.1837 | 0.1005 | 0.1414 | 0.3927 | 0.3601 | 0.4293 |
calm2-7b-chat-dpo-experimental | 0.3493 | 0.5312 | 0.5237 | 0.1857 | 0.1000 | 0.1813 | 0.3355 | 0.4320 | 0.5051 |
RakutenAI-7B-instruct | 0.2994 | 0.3623 | 0.3711 | 0.3333 | 0.1763 | 0.1581 | 0.4215 | 0.2824 | 0.2901 |
RakutenAI-7B-chat | 0.3667 | 0.4229 | 0.4644 | 0.3990 | 0.2161 | 0.2390 | 0.3416 | 0.3904 | 0.4601 |
13B models
Model | Average | Writing | Roleplay | Reasoning | Math | Coding | Extraction | STEM | Humanities |
---|---|---|---|---|---|---|---|---|---|
Swallow-13b-instruct-v0.1 | 0.3669 | 0.4816 | 0.5562 | 0.2769 | 0.1020 | 0.1505 | 0.4179 | 0.4347 | 0.5150 |
ELYZA-japanese-Llama-2-13b-instruct | 0.3196 | 0.4400 | 0.4373 | 0.2098 | 0.2157 | 0.1572 | 0.3583 | 0.3243 | 0.4141 |
ELYZA-japanese-Llama-2-13b-fast-instruct | 0.3042 | 0.3729 | 0.3930 | 0.1236 | 0.2492 | 0.1862 | 0.4360 | 0.3233 | 0.3496 |
70B models
Model | Average | Writing | Roleplay | Reasoning | Math | Coding | Extraction | STEM | Humanities |
---|---|---|---|---|---|---|---|---|---|
Swallow-70b-instruct-v0.1 | 0.4513 | 0.4822 | 0.5353 | 0.3497 | 0.3492 | 0.2668 | 0.5553 | 0.4955 | 0.5767 |
japanese-stablelm-instruct-beta-70b | 0.3716 | 0.4179 | 0.3945 | 0.3656 | 0.2580 | 0.2186 | 0.4412 | 0.4663 | 0.4103 |
Evaluation Benchmarks
MT-Bench JA
We used Japanese MT-Bench to assess the instruction-following capabilities of models. We utilized the following settings:
- Implemantation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3
- Reference Answer: Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1
- Prompt for Judge: Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1
- Judge:
gpt-4-1106-preview
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
Usage
First install additional dependencies in requirements.txt:
pip install -r requirements.txt
Instruction format Ver0.1
This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.
The template used to construct a prompt for the Instruct model is specified as follows:
<s>[INST] <<SYS>>\n{SYSTEM_PROMPT}\n<</SYS>>\n\n{USER_MESSAGE_1} [/INST] {BOT_MESSAGE_1}</s>[INST] {USER_MESSAGE_2} [/INST]
Please be aware that <s>
and </s>
are special tokens used for the beginning of string (BOS) and end of string (EOS), respectively, while [INST] and [/INST] are considered regular strings.
For the "{SYSTEM_PROMPT}" part, We recommend using "あなたは誠実で優秀な日本人のアシスタントです。"
For the "{USER_MESSAGE_1}" part, We recommend using {instruction}\n{input}
In other words, We recommend the following:
<s>[INST] <<SYS>>\nあなたは誠実で優秀な日本人のアシスタントです。\n<</SYS>>\n\n{instruction1}\n{input1} [/INST] {BOT_MESSAGE_1}</s>[INST] {instruction2}\n{input2} [/INST]
Use the instruct model Ver0.1
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "tokyotech-llm/Swallow-70b-instruct-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda"
messages = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{"role": "user", "content": "東京工業大学の主なキャンパスについて教えてください"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=128, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
Training Datasets
Instruction Tuning Ver0.1
The following datasets were used for the instruction tuning.
- OpenAssistant Conversations Dataset EN top-1 thread
- OpenAssistant Conversations Dataset was used, where human utterances are included but the responses are not used. Instead, the responses were generated using the Mixtral-8x7B-Instruct-v0.1 model.
Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
Acknowledgements
We thank Meta Research for releasing Llama 2 under an open license for others to build on.
Our project is supported by the ABCI Large-scale Language Model Building Support Program of the National Institute of Advanced Industrial Science and Technology.
License
Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
Authors
Here are the team members:
- From Okazaki Laboratory, the following members:
- From YOKOTA Laboratory, the following members:
How to cite
If you find our work helpful, please feel free to cite us.
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
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