--- language: - en - ja library_name: transformers pipeline_tag: text-generation license: llama2 model_type: llama --- # Swallow Our Swallow model has undergone continuous pre-training from the [Llama 2 family](https://huggingface.co./meta-llama), 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 25, 2024**: Released version 1.0 of our enhanced instruction-tuned models: [Swallow-7b-instruct-v1.0](https://huggingface.co./tokyotech-llm/Swallow-7b-instruct-v1.0), [Swallow-13b-instruct-v1.0](https://huggingface.co./tokyotech-llm/Swallow-13b-instruct-v1.0), and [Swallow-70b-instruct-v1.0](https://huggingface.co./tokyotech-llm/Swallow-70b-instruct-v1.0). - **March 2, 2024**: Released the [Swallow-7b-plus-hf](https://huggingface.co./tokyotech-llm/Swallow-7b-plus-hf), a model trained with approximately twice as many Japanese tokens as [Swallow-7b-hf](https://huggingface.co./tokyotech-llm/Swallow-7b-hf). - **February 4, 2024**: Released the [Swallow-13b-NVE-hf](https://huggingface.co./tokyotech-llm/Swallow-13b-NVE-hf). - **January 26, 2024**: Released the [Swallow-7b-NVE-hf](https://huggingface.co./tokyotech-llm/Swallow-7b-NVE-hf), [Swallow-7b-NVE-instruct-hf](https://huggingface.co./tokyotech-llm/Swallow-7b-NVE-instruct-hf), [Swallow-70b-NVE-hf](https://huggingface.co./tokyotech-llm/Swallow-70b-NVE-hf), and [Swallow-70b-NVE-instruct-hf](https://huggingface.co./tokyotech-llm/Swallow-70b-NVE-instruct-hf) - **December 19, 2024**: Released the [Swallow-7b-hf](https://huggingface.co./tokyotech-llm/Swallow-7b-hf), [Swallow-7b-instruct-hf](https://huggingface.co./tokyotech-llm/Swallow-7b-instruct-hf), [Swallow-13b-hf](https://huggingface.co./tokyotech-llm/Swallow-13b-hf), [Swallow-13b-instruct-hf](https://huggingface.co./tokyotech-llm/Swallow-13b-instruct-hf), [Swallow-70b-hf](https://huggingface.co./tokyotech-llm/Swallow-70b-hf), and [Swallow-70b-instruct-hf](https://huggingface.co./tokyotech-llm/Swallow-70b-instruct-hf). ## Swallow Model Index |Model|Swallow-hf|Swallow-instruct-hf|Swallow-instruct-v1.0| |---|---|---|---| |7B| [Link](https://huggingface.co./tokyotech-llm/Swallow-7b-hf) | [Link](https://huggingface.co./tokyotech-llm/Swallow-7b-instruct-hf)|[Link](https://huggingface.co./tokyotech-llm/Swallow-7b-instruct-v1.0)| |7B-Plus| [Link](https://huggingface.co./tokyotech-llm/Swallow-7b-plus-hf) | N/A | N/A | |13B| [Link](https://huggingface.co./tokyotech-llm/Swallow-13b-hf) | [Link](https://huggingface.co./tokyotech-llm/Swallow-13b-instruct-hf)| [Link](https://huggingface.co./tokyotech-llm/Swallow-13b-instruct-v1.0)| |70B| [Link](https://huggingface.co./tokyotech-llm/Swallow-70b-hf) | [Link](https://huggingface.co./tokyotech-llm/Swallow-70b-instruct-hf)| [Link](https://huggingface.co./tokyotech-llm/Swallow-70b-instruct-v1.0)| ## Swallow Model Index NVE (No Vocabulary Expansion) |Model|Swallow-NVE-hf|Swallow-NVE-instruct-hf| |---|---|---| |7B| [Link](https://huggingface.co./tokyotech-llm/Swallow-7b-NVE-hf) | [Link](https://huggingface.co./tokyotech-llm/Swallow-7b-NVE-instruct-hf)| |13B| [Link](https://huggingface.co./tokyotech-llm/Swallow-13b-NVE-hf) | N/A | |70B| [Link](https://huggingface.co./tokyotech-llm/Swallow-70b-NVE-hf) | [Link](https://huggingface.co./tokyotech-llm/Swallow-70b-NVE-instruct-hf)| ![logo](./logo.png) This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/). Read our [blog post](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) or our [paper](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/A8-5.pdf) ## Model Details * **Model type**: Please refer to LLaMA-2 technical report for details on the model architecture. * **Language(s)**: Japanese English * **Library**: [Megatron-LM](https://github.com/rioyokotalab/Megatron-Llama2) * **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 TODO ## Base Model Performance ### Japanese tasks |Model|Size|JCommonsenseQA|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en| |---|---|---|---|---|---|---|---|---|---| | | |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot| | Llama 2 | 7B | 0.3852 | 0.4240 | 0.3410 | 0.7917 | 0.1905 | 0.0760 | 0.1783 | 0.1738 | | Swallow | 7B | 0.4808 | 0.5078 | 0.5968 | 0.8573 | 0.1830 | 0.1240 | 0.2510 | 0.1511 | | Swallow-Plus | 7B | 0.5478 | 0.5493 | 0.6030 | 0.8544 | 0.1806 | 0.1360 | 0.2568 | 0.1441 | | Swallow-NVE | 7B | 0.5433 | 0.5425 | 0.5729 | 0.8684 | 0.2117 | 0.1200 | 0.2405 | 0.1512 | | Llama 2 | 13B | 0.6997 | 0.4415 | 0.4170 | 0.8533 | 0.2139 | 0.1320 | 0.2146 | 0.1982 | | Swallow | 13B | 0.7837 | 0.5063 | 0.6398 | 0.9005 | 0.2168 | 0.2040 | 0.2720 | 0.1771 | | Swallow-NVE | 13B | 0.7712 | 0.5438 | 0.6351 | 0.9030 | 0.2294 | 0.2120 | 0.2735 | 0.1817 | | Llama 2 | 70B | 0.8686 | 0.4656 | 0.5256 | 0.9080 | 0.2361 | 0.3560 | 0.2643 | **0.2398** | | Swallow | 70B | 0.9348 | **0.6290** | 0.6960 | 0.9176 | 0.2266 | **0.4840** | **0.3043** | 0.2298 | | Swallow-NVE | 70B | **0.9410** | 0.5759 | **0.7024** | **0.9254** | **0.2758** | 0.4720 | 0.3042 | 0.2322 | ### English tasks |Model|Size|OpenBookQA|TriviaQA|HellaSwag|SQuAD2.0|XWINO|GSM8K| |---|---|---|---|---|---|---|---| | | |8-shot|8-shot|8-shot|8-shot|8-shot|8-shot| | Llama 2 | 7B | 0.3580 | 0.6265 | 0.5860 | 0.3207 | 0.9049 | 0.1410 | | Swallow | 7B | 0.3180 | 0.4836 | 0.5308 | 0.3125 | 0.8817 | 0.1130 | | Swallow-Plus | 7B | 0.3280 | 0.4558 | 0.5259 | 0.3134 | 0.8929 | 0.1061 | | Swallow-NVE | 7B | 0.3180 | 0.5079 | 0.5329 | 0.2919 | 0.8817 | 0.0986 | | Llama 2 | 13B | 0.3760 | 0.7255 | 0.6148 | 0.3681 | 0.9140 | 0.2403 | | Swallow | 13B | 0.3500 | 0.5852 | 0.5660 | 0.3406 | 0.9075 | 0.2039 | | Swallow-NVE | 13B | 0.3460 | 0.6025 | 0.5700 | 0.3478 | 0.9006 | 0.1751 | | Llama 2 | 70B | **0.4280** | **0.8239** | **0.6742** | **0.3770** | **0.9290** | **0.5284** | | Swallow | 70B | 0.4220 | 0.7756 | 0.6458 | 0.3745 | 0.9204 | 0.4867 | | Swallow-NVE | 70B | 0.4240 | 0.7817 | 0.6439 | 0.3451 | 0.9256 | 0.4943 | ## Evaluation Benchmarks ### Japanese evaluation benchmarks We used llm-jp-eval(v1.0.0) and JP Language Model Evaluation Harness(commit #9b42d41). The details are as follows: - Multiple-choice question answering (JCommonsenseQA [Kurihara+, 2022]) - Open-ended question answering (JEMHopQA [Ishii+, 2023]) - Open-ended question answering (NIILC [Sekine, 2003]) - Machine reading comprehension (JSQuAD [Kurihara+, 2022]) - Automatic summarization (XL-Sum [Hasan+, 2021]) - Machine translation (WMT2020 ja-en [Barrault+, 2020]) - Machine translation (WMT2020 en-ja [Barrault+, 2020]) - Mathematical reasoning (MGSM [Shi+, 2023]) ### English evaluation benchmarks We used the Language Model Evaluation Harness(v.0.3.0). The details are as follows: - Multiple-choice question answering (OpenBookQA [Mihaylov+, 2018]) - Open-ended question answering (TriviaQA [Joshi+, 2017]) - Machine reading comprehension (SQuAD 2.0 [Rajpurkar+, 2018]) - Commonsense reasoning (XWINO [Tikhonov & Ryabinin, 2021]) - Natural language inference (HellaSwag [Zellers+, 2019]) - Mathematical reasoning (GSM8k [Cobbe+, 2021]) ## Usage First install additional dependencies in [requirements.txt](./requirements.txt): ```sh pip install -r requirements.txt ``` ### Instruction format Ver1.0 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: ``` [INST] <>\n{Instruction}\n<>\n\n{USER_MESSAGE_1} [INST] {BOT_MESSAGE_1} [INST] {USER_MESSAGE_2}[/INST] ``` Please be aware that and are special tokens used for the beginning of string (BOS) and end of string (EOS), respectively, while [INST] and [/INST] are considered regular strings. ### Use the instruct model Ver1.0 ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-70b-instruct-v1.0" 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]) ``` ### Use the instruct model **Note:** Please be aware that the inference example is based on a model version older than 1.0. ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-7b-instruct-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto") PROMPT_DICT = { "prompt_input": ( "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:" ), "prompt_no_input": ( "以下に、あるタスクを説明する指示があります。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 応答:" ), } def create_prompt(instruction, input=None): """ Generates a prompt based on the given instruction and an optional input. If input is provided, it uses the 'prompt_input' template from PROMPT_DICT. If no input is provided, it uses the 'prompt_no_input' template. Args: instruction (str): The instruction describing the task. input (str, optional): Additional input providing context for the task. Default is None. Returns: str: The generated prompt. """ if input: # Use the 'prompt_input' template when additional input is provided return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input) else: # Use the 'prompt_no_input' template when no additional input is provided return PROMPT_DICT["prompt_no_input"].format(instruction=instruction) # Example usage instruction_example = "以下のトピックに関する詳細な情報を提供してください。" input_example = "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" prompt = create_prompt(instruction_example, input_example) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ### Use the base model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") prompt = "東京工業大学の主なキャンパスは、" input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## Training Datasets ### Continual Pre-Training The following datasets were used for continual pre-training. - [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) - [RefinedWeb](https://huggingface.co./datasets/tiiuae/falcon-refinedweb) - Swallow Corpus - [The Pile](https://huggingface.co./datasets/EleutherAI/pile) ### Instruction Tuning #### Ver1.0 The following datasets were used for the instruction tuning. - [OpenAssistant Conversations Dataset EN top-1 thread](https://huggingface.co./datasets/OpenAssistant/oasst2) - [OpenAssistant Conversations Dataset](https://huggingface.co./datasets/llm-jp/oasst1-21k-ja) 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](https://huggingface.co./datasets/llm-jp/oasst1-21k-jahttps://huggingface.co./mistralai/Mixtral-8x7B-Instruct-v0.1) model. #### Old The following datasets were used for the instruction tuning. - [Anthropic HH-RLHF](https://huggingface.co./datasets/kunishou/hh-rlhf-49k-ja) - [Databricks Dolly 15-k](https://huggingface.co./datasets/kunishou/databricks-dolly-15k-ja) - [OpenAssistant Conversations Dataset](https://huggingface.co./datasets/kunishou/oasst1-89k-ja) ## 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](https://abci.ai/en/link/llm_support_program.html) 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](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Hiroki Iida](https://meshidenn.github.io/) - [Mengsay Loem](https://loem-ms.github.io/) - [Shota Hirai](https://huggingface.co./Kotemo428) - [Kakeru Hattori](https://aya-se.vercel.app/) - [Masanari Ohi](https://twitter.com/stjohn2007) - From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2) - [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto) - [Ishida Shigeki](https://www.wantedly.com/id/reborn27)