richarderkhov/tokyotech-llm_-_swallow-ms-7b-v0.1-gguf overview

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    "summary": "Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data. # Model Release Updates We are excited to share the release schedule for our latest models: !logo This repository provides large language models developed by TokyoTech-LLM.",
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    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nSwallow-MS-7b-v0.1 - GGUF\n- Model creator: https://huggingface.co/tokyotech-llm/\n- Original model: https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [Swallow-MS-7b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q2_K.gguf) | Q2_K | 2.58GB |\n| [Swallow-MS-7b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ3_XS.gguf) | IQ3_XS | 2.86GB |\n| [Swallow-MS-7b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ3_S.gguf) | IQ3_S | 3.02GB |\n| [Swallow-MS-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.0GB |\n| [Swallow-MS-7b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ3_M.gguf) | IQ3_M | 3.11GB |\n| [Swallow-MS-7b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K.gguf) | Q3_K | 3.33GB |\n| [Swallow-MS-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.33GB |\n| [Swallow-MS-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.61GB |\n| [Swallow-MS-7b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ4_XS.gguf) | IQ4_XS | 3.73GB |\n| [Swallow-MS-7b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_0.gguf) | Q4_0 | 3.88GB |\n| [Swallow-MS-7b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.IQ4_NL.gguf) | IQ4_NL | 3.93GB |\n| [Swallow-MS-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_K_S.gguf) | Q4_K_S | 3.91GB |\n| [Swallow-MS-7b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_K.gguf) | Q4_K | 4.13GB |\n| [Swallow-MS-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.13GB |\n| [Swallow-MS-7b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q4_1.gguf) | Q4_1 | 4.3GB |\n| [Swallow-MS-7b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_0.gguf) | Q5_0 | 4.72GB |\n| [Swallow-MS-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_K_S.gguf) | Q5_K_S | 4.72GB |\n| [Swallow-MS-7b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_K.gguf) | Q5_K | 4.84GB |\n| [Swallow-MS-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_K_M.gguf) | Q5_K_M | 4.84GB |\n| [Swallow-MS-7b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q5_1.gguf) | Q5_1 | 5.13GB |\n| [Swallow-MS-7b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q6_K.gguf) | Q6_K | 5.6GB |\n| [Swallow-MS-7b-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-MS-7b-v0.1-gguf/blob/main/Swallow-MS-7b-v0.1.Q8_0.gguf) | Q8_0 | 7.26GB |\n\n\n\n\nOriginal model description:\n---\nlanguage:\n  - en\n  - ja\nlibrary_name: transformers\npipeline_tag: text-generation\nmodel_type: mistral\nlicense: apache-2.0\n---\n\n# Swallow-MS-7b-v0.1\n\nOur Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data. \n\n# Model Release Updates\n\nWe are excited to share the release schedule for our latest models:\n- **April 26, 2024**: Released the [Swallow-MS-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1)\n- **March 11, 2024**: Released the [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)\n![logo](./logo.png)\n\nThis repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/).\n\n## Model Details\n\n* **Model type**: Please refer to Mistral technical report for details on the model architecture. \n* **Language(s)**: Japanese English\n* **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.\n* **Contact**: swallow[at]nlp.c.titech.ac.jp \n\n\n## Base Model Performance\n\n### Japanese tasks\n|Model|Size|JCommonsenseQA|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|Average|\n|---------------------------|-------|---------|-------|-------|-------|------|------------|------------|------|-----|\n|   |   |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|| \n| CyberAgentLM2-7B |7B| 0.2198 | 0.5047 | 0.5066 | 0.7799 | 0.0233 | 0.0600 | 0.2345 | 0.1499 | 0.3098 |\n| Llama 2  |7B| 0.3852 | 0.4240 | 0.3410 | 0.7917 | 0.1905 | 0.0760 | 0.1783 | 0.1738 | 0.3201 |\n| japanese-stablelm-base-beta-7b|7B| 0.3610 | 0.4478 | 0.4432 | 0.8318 | 0.2195 | 0.0720 | 0.1946 | 0.1226 | 0.3366 |\n| japanese-stablelm-base-ja_vocab-beta-7b|7B| 0.2172 | 0.4482 | 0.4309 | 0.8202 | 0.0757 | 0.0520 | 0.1601 | 0.1453 | 0.2937 |\n| ELYZA-japanese-Llama-2-7b|7B| 0.5791 | 0.4703 | 0.4019 | 0.8226 | 0.1312 | 0.0600 | 0.1795 | 0.1289 | 0.3467 |\n| ELYZA-japanese-Llama-2-7b-fast|7B| 0.5308 | 0.4330 | 0.3898 | 0.8131 | 0.1289 | 0.0720 | 0.1678 | 0.1143 | 0.3312 |\n| youri-7b (base) |7B| 0.4620 | 0.4776 | 0.4999 | 0.8506 | 0.1957 | 0.0640 | 0.2671 | **0.1971** | 0.3768 |\n| Swallow-7b |7B| 0.4808 | 0.5078 | 0.5968 | 0.8573 | 0.1830 | 0.1240 | 0.2510 | 0.1511 | 0.3940 |\n| Swallow-7b-plus |7B| 0.5478 | **0.5493** | **0.6030** | 0.8544 | 0.1806 | 0.1360 | 0.2568 | 0.1441 | 0.4090 |\n| Qwen-7B |7B| 0.7712 | 0.4234 | 0.2376 | 0.8594 | 0.1371 | 0.2160 | 0.1689 | 0.1801 | 0.3742 |\n| nekomata-7b |7B| 0.7417 | 0.4928 | 0.5022 | 0.8707 | 0.1676 | 0.1240 | **0.2673** | 0.1815 | 0.4185 |\n| Mistral-7B-v0.1 |7B| 0.7301 | 0.4245 | 0.2722 | 0.8563 | 0.2006 | 0.1760 | 0.1405 | 0.1733 | 0.3717 |\n| japanese-stablelm-base-gamma-7b|7B| 0.7364 | 0.4643 | 0.5568 | **0.8910** | **0.2293** | 0.1680 | 0.2390 | 0.1561 | 0.4301 |\n| Swallow-MS-7b-v0.1 |7B| **0.8570** | 0.4915 | 0.5519 | 0.8802 | 0.1988 | **0.2240** | 0.2494 | 0.1667 | **0.4524** |\n\n\n### English tasks\n\n|Model|Size|OpenBookQA|TriviaQA|HellaSwag|SQuAD2.0|XWINO|GSM8K|Average|\n|---|---|---|---|---|---|---|---|---|\n|   |   |8-shot|8-shot|8-shot|8-shot|8-shot|8-shot||\n| CyberAgentLM2-7B |7B| 0.2860 | 0.3496 | 0.5003 | 0.3510 | 0.8581 | 0.0705 | 0.4026 |\n| Llama 2  |7B| 0.3580 | 0.6265 | 0.5860 | 0.3207 | 0.9049 | 0.1410 | 0.4895 |\n| japanese-stablelm-base-beta-7b|7B| 0.3620 | 0.5903 | 0.5707 | 0.2992 | 0.8994 | 0.1198 | 0.4736 |\n| japanese-stablelm-base-ja_vocab-beta-7b|7B| 0.3520 | 0.5549 | 0.5644 | 0.3079 | 0.8942 | 0.0538 | 0.4545 |\n| ELYZA-japanese-Llama-2-7b|7B| 0.3400 | 0.5875 | 0.5595 | 0.2721 | 0.8989 | 0.1638 | 0.4703 |\n| ELYZA-japanese-Llama-2-7b-fast|7B| 0.3280 | 0.5817 | 0.5530 | 0.2605 | 0.8989 | 0.1425 | 0.4608 |\n| youri-7b (base) |7B| 0.3400 | 0.5257 | 0.5540 | 0.3297 | 0.8938 | 0.0963 | 0.4566 |\n| Swallow-7b |7B| 0.3180 | 0.4836 | 0.5308 | 0.3125 | 0.8817 | 0.1130 | 0.4399 |\n| Swallow-7b-plus |7B| 0.3280 | 0.4558 | 0.5259 | 0.3134 | 0.8929 | 0.1061 | 0.4370 |\n| Qwen-7B |7B| 0.3640 | 0.5695 | 0.5787 | **0.3799** | 0.8933 | **0.4617** | 0.5412 |\n| nekomata-7b |7B| 0.3340 | 0.4371 | 0.5340 | 0.2933 | 0.8766 | 0.1531 | 0.4380 |\n| Mistral-7B-v0.1 |7B| **0.3660** | **0.7050** | **0.6264** | **0.3799** | **0.9157** | 0.3533 | **0.5577** |\n| japanese-stablelm-base-gamma-7b|7B| 0.3240 | 0.5745 | 0.5739 | 0.3546 | 0.8976 | 0.1911 | 0.4860 |\n| Swallow-MS-7b-v0.1 |7B| 0.3440 | 0.5976 | 0.5810 | 0.3364 | 0.9037 | 0.2623 | 0.5042 |\n\n\n### Code generation tasks\n\n|Model|Size|JHumanEval|HumanEval|\n|---|---|---|---|\n|   |   |pass@1|pass@1|\n| CyberAgentLM2-7B |7B|0.0634|0.0756|\n| Llama 2  |7B|0.1152|0.1378|\n| japanese-stablelm-base-beta-7b|7B|0.1018|0.1280|\n| japanese-stablelm-base-ja_vocab-beta-7b|7B|0.0896|0.1122|\n| ELYZA-japanese-Llama-2-7b|7B|0.0287|0.0427|\n| ELYZA-japanese-Llama-2-7b-fast|7B| 0.0000\t|0.0037|\n| youri-7b (base) |7B|0.0829|0.0982|\n| Swallow-7b |7B|0.0183|0.0183|\n| Swallow-7b-plus |7B| 0.0061|0.0037|\n| Qwen-7B |7B|0.1701|0.1805|\n| nekomata-7b |7B|0.0988|0.1402|\n| Mistral-7B-v0.1 |7B|**0.2555**|**0.2933**|\n| japanese-stablelm-base-gamma-7b|7B|0.1823|0.1915|\n| Swallow-MS-7b-v0.1 |7B|0.2305|0.2768|\n\n## Evaluation Benchmarks\n\n### Japanese evaluation benchmarks\n\nWe used llm-jp-eval(v1.0.0) and JP Language Model Evaluation Harness(commit #9b42d41). The details are as follows:\n\n- Multiple-choice question answering (JCommonsenseQA [Kurihara+, 2022])\n- Open-ended question answering (JEMHopQA [Ishii+, 2023])\n- Open-ended question answering (NIILC [Sekine, 2003])\n- Machine reading comprehension (JSQuAD [Kurihara+, 2022])\n- Automatic summarization (XL-Sum [Hasan+, 2021])\n- Machine translation (WMT2020 ja-en [Barrault+, 2020])\n- Machine translation (WMT2020 en-ja [Barrault+, 2020])\n- Mathematical reasoning (MGSM [Shi+, 2023])\n\n### English evaluation benchmarks\n\nWe used the Language Model Evaluation Harness(v.0.3.0). The details are as follows:\n\n- Multiple-choice question answering (OpenBookQA [Mihaylov+, 2018])\n- Open-ended question answering (TriviaQA [Joshi+, 2017])\n- Machine reading comprehension (SQuAD 2.0 [Rajpurkar+, 2018])\n- Commonsense reasoning (XWINO [Tikhonov & Ryabinin, 2021])\n- Natural language inference (HellaSwag [Zellers+, 2019])\n- Mathematical reasoning (GSM8k [Cobbe+, 2021])\n\n### Code evaluation benchmarks\n\nWe utilized the Code Generation LM Evaluation Harness [Allal+, 2022] (commit #0261c52). The details are as follows:\n\n- Code generation (HumanEval [Chen+, 2021])\n- Code generation in Japanese (JHumanEval [Satoh+, 2024])\n\n\n## Usage\n\nFirst install additional dependencies in [requirements.txt](./requirements.txt):\n\n```sh\npip install -r requirements.txt\n```\n\n### Use the base model\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\n\nmodel_name = \"tokyotech-llm/Swallow-MS-7b-v0.1\"\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\nmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map=\"auto\")\nprompt = \"東京工業大学の主なキャンパスは、\"\ninput_ids = tokenizer.encode(\n    prompt,\n    add_special_tokens=False,\n    return_tensors=\"pt\"\n)\ntokens = model.generate(\n    input_ids.to(device=model.device),\n    max_new_tokens=128,\n    temperature=0.99,\n    top_p=0.95,\n    do_sample=True,\n)\n\nout = tokenizer.decode(tokens[0], skip_special_tokens=True)\nprint(out)\n```\n\n## Training Datasets\n\n### Continual Pre-Training\nThe following datasets were used for continual pre-training.\n\n- [Algebraic Stack](https://huggingface.co/datasets/EleutherAI/proof-pile-2)\n- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)\n- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)\n- [Swallow Corpus](https://arxiv.org/abs/2404.17733)\n- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)\n\n## Risks and Limitations\n\nThe 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.\n\n## Acknowledgements\n\nWe thank Mistral AI for releasing Mistral 7B v0.1 under an open license for others to build on.\n\nOur 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. \n\n## License\n\napache-2.0\n\n## Authors\n\nHere are the team members:\n- From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:\n  - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)\n  - [Sakae Mizuki](https://s-mizuki-nlp.github.io/)\n  - [Hiroki Iida](https://meshidenn.github.io/)\n  - [Mengsay Loem](https://loem-ms.github.io/)\n  - [Shota Hirai](https://huggingface.co/Kotemo428)\n  - [Kakeru Hattori](https://aya-se.vercel.app/)\n  - [Masanari Ohi](https://twitter.com/stjohn2007)\n- From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:\n  - [Rio Yokota](https://twitter.com/rioyokota)\n  - [Kazuki Fujii](https://twitter.com/okoge_kaz)\n  - [Taishi Nakamura](https://twitter.com/Setuna7777_2)\n\n\n",
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