Upload README.md

README.md

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+---
+language:
+  - en
+  - ja
+library_name: transformers
+pipeline_tag: text-generation
+license: llama3
+model_type: llama
+---
+
+# Llama3 Swallow
+
+Our Swallow model has undergone continual pre-training from the [Llama 3 family](https://huggingface.co/collections/meta-llama/meta-llama-3-66214712577ca38149ebb2b6), primarily with the addition of Japanese language data. The Instruct versions use supervised fine-tuning (SFT) and Chat Vector. 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:
+- **July 1, 2024**: Released the [Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1), [Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1), [Llama-3-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-v0.1), and [Llama-3-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-Instruct-v0.1).
+
+## Swallow Model Index
+
+|Model|Llama-3-Swallow|Llama3 Swallow instruct|
+|---|---|---|
+|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1) |
+|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-Instruct-v0.1) |
+
+![logo](./logo.png)
+
+This repository provides large language models developed by [Swallow-LLM](https://swallow-llm.github.io/).
+Read our [blog post](https://zenn.dev/tokyotech_lm/articles/f65989d76baf2c).
+
+## Model Details
+
+* **Model type**: Please refer to [Llama 3 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
+* **Language(s)**: Japanese English
+* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 
+* **Tokenizer**: Please refer to [Llama 3 blog](https://ai.meta.com/blog/meta-llama-3/) for details on the tokenizer.
+* **Contact**: swallow[at]nlp.c.titech.ac.jp 
+
+## Model Performance
+
+### Japanese tasks
+
+|Model|Size|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
+|---|---|---|---|---|---|---|---|---|---|---|---|---|
+|   |   |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot|   |
+|   |   |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1|   |
+|calm2-7b-chat|7B|0.2413|0.5128|0.4956|0.7729|0.0551|0.0480|0.2208|0.1384|0.2482|0.0000|0.2733|
+|Swallow-7b-instruct-v0.1|7B|0.6059|0.4760|0.5284|0.8396|0.1546|0.1360|0.2285|0.1783|0.3510|0.0256|0.3524|
+|Swallow-MS-7b-instruct-v0.1|7B|0.7435|0.5066|0.4268|0.8594|0.1582|0.1760|0.2260|0.1880|0.4177|0.2244|0.3927|
+|RakutenAI-7B-chat|7B|0.9035|0.2600|0.4619|0.8647|0.1339|0.2120|0.2667|0.1966|0.4504|0.2299|0.3980|
+|Qwen2-7B-Instruct|7B|0.8856|0.3902|0.3859|0.8967|0.1277|0.5720|0.2041|0.1909|0.5713|0.5683|0.4793|
+|Meta-Llama-3-8B-Instruct|8B|0.8785|0.3812|0.3936|0.8955|0.1273|0.4160|0.2143|0.2035|0.4719|0.2872|0.4269|
+|Llama-3-ELYZA-JP-8B|8B|0.9017|0.5124|0.5016|0.9113|0.1677|0.4600|0.2509|0.1846|0.4829|0.3811|0.4754|
+|Llama-3-Swallow-8B-Instruct-v0.1|8B|0.9178|0.4963|0.5168|0.9088|0.1296|0.4880|0.2522|0.2254|0.4835|0.3927|0.4811|
+
+### English tasks
+
+|Model|Size|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
+|---|---|---|---|---|---|---|---|---|---|---|---|
+|   |   |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot|   |
+|   |   |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1|   |
+|calm2-7b-chat|7B|0.2860|0.3528|0.5042|0.2524|0.8413|0.3860|0.0546|0.2990|0.0000|0.3307|
+|Swallow-7b-instruct-v0.1|7B|0.3280|0.4810|0.5501|0.2720|0.8774|0.4066|0.1251|0.3646|0.0866|0.3879|
+|Swallow-MS-7b-instruct-v0.1|7B|0.3600|0.4999|0.5858|0.3030|0.8834|0.5273|0.2108|0.4386|0.2512|0.4511|
+|RakutenAI-7B-chat|7B|0.4160|0.5971|0.6465|0.3091|0.8886|0.5757|0.3139|0.4958|0.2671|0.5011|
+|Qwen2-7B-Instruct|7B|0.4000|0.5468|0.6146|0.3518|0.8852|0.7073|0.6300|0.3101|0.6354|0.5646|
+|Meta-Llama-3-8B-Instruct|8B|0.3880|0.6687|0.5834|0.3743|0.8903|0.6567|0.7453|0.6478|0.5415|0.6107|
+|Llama-3-ELYZA-JP-8B|8B|0.3200|0.5502|0.5224|0.3631|0.8809|0.5875|0.5701|0.3213|0.4604|0.5084|
+|Llama-3-Swallow-8B-Instruct-v0.1|8B|0.3720|0.6557|0.5861|0.3648|0.9002|0.6315|0.5959|0.6391|0.4238|0.5743|
+
+## MT-Bench JA
+
+|Model|Size|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
+|---|---|---|---|---|---|---|---|---|---|---|---|---|
+|calm2-7b-chat|7B|0.1198|0.3793|0.4231|0.1011|0.1799|0.4760|0.3568|0.4583|0.3118|
+|Swallow-7b-instruct-v0.1|7B|0.1947|0.3156|0.4991|0.1900|0.2141|0.5330|0.4535|0.4624|0.3578|
+|Swallow-MS-7b-instruct-v0.1|7B|0.2235|0.3743|0.4611|0.1060|0.3404|0.4287|0.3969|0.3877|0.3398|
+|RakutenAI-7B-chat|7B|0.2475|0.3522|0.4692|0.2140|0.3926|0.4427|0.3977|0.4434|0.3699|
+|Qwen2-7B-Instruct|7B|0.4635|0.6909|0.6857|0.5970|0.5042|0.6667|0.5353|0.6808|0.6030|
+|Meta-Llama-3-8B-Instruct|8B|0.3744|0.6876|0.6225|0.2070|0.5032|0.5248|0.5326|0.4884|0.4926|
+|Llama-3-ELYZA-JP-8B|8B|0.2908|0.6421|0.6406|0.3088|0.5500|0.6740|0.5251|0.6744|0.5382|
+|Llama-3-Swallow-8B-Instruct-v0.1|8B|0.3547|0.6508|0.5371|0.2718|0.4007|0.5493|0.4752|0.5730|0.4766|
+
+## Evaluation Benchmarks
+
+### Japanese evaluation benchmarks
+
+We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
+
+- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
+- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
+- Open-ended question answering (NIILC [関根, 2003])
+- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
+- Automatic summarization (XL-Sum [Hasan et al., 2021])
+- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
+- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
+- Mathematical reasoning (MGSM [Shi et al., 2023])
+- Academic exams (JMMLU [尹ら, 2024])
+- Code generation (JHumanEval [佐藤ら, 2024])
+
+### English evaluation benchmarks
+
+We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
+
+- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
+- Open-ended question answering (TriviaQA [Joshi et al., 2017])
+- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
+- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
+- Natural language inference (HellaSwag [Zellers et al., 2019])
+- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
+- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
+- Academic exams (MMLU [Hendrycks et al., 2021])
+- Code generation (HumanEval [Chen et al., 2021])
+
+### MT-Bench JA
+
+We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) 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](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
+- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
+- Prompt for Judge: [Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
+- Judge: `gpt-4-1106-preview`
+- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
+
+## Usage
+
+```sh
+pip install vllm
+```
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_name = "tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(
+    model=model_name,
+    tensor_parallel_size=1,
+)
+
+sampling_params = SamplingParams(
+    temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
+)
+
+
+message = [
+    {"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": "東京の夜空に打ち上がっている花火の下、向かい合っている燕とラマの温かい物語を書いてください。",
+    },
+]
+prompt = tokenizer.apply_chat_template(
+    message, tokenize=False, add_generation_prompt=True
+)
+
+output = llm.generate(prompt, sampling_params)
+
+print(output[0].outputs[0].text)
+
+```
+
+## Training Datasets
+
+### Instruction Tuning
+
+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/mistralai/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 3 under an open license for others to build on.
+
+Our project is supported by the [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html) of the National Institute of Advanced Industrial Science and Technology. 
+
+## License
+
+[META LLAMA 3 COMMUNITY LICENSE](https://llama.meta.com/llama3/license/)
+
+## Authors
+
+Here are the team members:
+- From [Tokyo Institute of Technology 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/)
+  - [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
+  - [Koki Maeda](https://sites.google.com/view/silviase)
+  - [Kakeru Hattori](https://aya-se.vercel.app/)
+  - [Masanari Ohi](https://sites.google.com/view/masanariohi)
+  - [Taihei Shiotani](https://github.com/inatoihs)
+  - [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
+- From [Tokyo Institute of Technology 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)
+- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
+  - [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
+
+## How to Cite
+
+If you find our work helpful, please feel free to cite us.
+
+```tex
+@misc{llama3swallow,
+      title={Llama 3 Swallow},
+      url={https://swallow-llm.github.io/llama3-swallow.en.html},
+      author={Swallow LLM},
+      year={2024},
+}
+```
+
+### Citations
+
+```tex
+@article{llama3modelcard,
+    title={Llama 3 Model Card},
+    author={AI@Meta},
+    year={2024},
+    url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
+}
+```