richarderkhov/tokyotech-llm_-_swallow-70b-instruct-v0.1-gguf overview

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    "hero_image_url": "./logo.png",
    "summary": "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:",
    "quick_links": [],
    "benchmark_table_html": "",
    "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-70b-instruct-v0.1 - GGUF\n- Model creator: https://huggingface.co/tokyotech-llm/\n- Original model: https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v0.1/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [Swallow-70b-instruct-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q2_K.gguf) | Q2_K | 23.81GB |\n| [Swallow-70b-instruct-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.IQ3_XS.gguf) | IQ3_XS | 26.48GB |\n| [Swallow-70b-instruct-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.IQ3_S.gguf) | IQ3_S | 27.97GB |\n| [Swallow-70b-instruct-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 27.97GB |\n| [Swallow-70b-instruct-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.IQ3_M.gguf) | IQ3_M | 28.93GB |\n| [Swallow-70b-instruct-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q3_K.gguf) | Q3_K | 31.1GB |\n| [Swallow-70b-instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 31.1GB |\n| [Swallow-70b-instruct-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 33.77GB |\n| [Swallow-70b-instruct-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 34.76GB |\n| [Swallow-70b-instruct-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q4_0.gguf) | Q4_0 | 36.32GB |\n| [Swallow-70b-instruct-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.IQ4_NL.gguf) | IQ4_NL | 36.67GB |\n| [Swallow-70b-instruct-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/blob/main/Swallow-70b-instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 36.67GB |\n| [Swallow-70b-instruct-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q4_K | 38.7GB |\n| [Swallow-70b-instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q4_K_M | 38.7GB |\n| [Swallow-70b-instruct-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q4_1 | 40.33GB |\n| [Swallow-70b-instruct-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q5_0 | 44.33GB |\n| [Swallow-70b-instruct-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q5_K_S | 44.33GB |\n| [Swallow-70b-instruct-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q5_K | 45.53GB |\n| [Swallow-70b-instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q5_K_M | 45.53GB |\n| [Swallow-70b-instruct-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q5_1 | 48.34GB |\n| [Swallow-70b-instruct-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q6_K | 52.84GB |\n| [Swallow-70b-instruct-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/tokyotech-llm_-_Swallow-70b-instruct-v0.1-gguf/tree/main/) | Q8_0 | 68.44GB |\n\n\n\n\nOriginal model description:\n---\nlanguage:\n  - en\n  - ja\nlibrary_name: transformers\npipeline_tag: text-generation\nlicense: llama2\nmodel_type: llama\n---\n\n# Swallow\n\nOur Swallow model has undergone continual 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). \nLinks to other models can be found in the index.\n\n# Model Release Updates\n\nWe are excited to share the release schedule for our latest models:\n- **April 26, 2024**: Released version 0.1 of our enhanced instruction-tuned models: [Swallow-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-v0.1), [Swallow-13b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-v0.1), and [Swallow-70b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v0.1) as preview versions.\n- **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).\n- **February 4, 2024**: Released the [Swallow-13b-NVE-hf](https://huggingface.co/tokyotech-llm/Swallow-13b-NVE-hf).\n- **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)\n- **December 19, 2023**: 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).\n\n## Swallow Model Index\n|Model|Swallow-hf|Swallow-instruct-hf|Swallow-instruct-v0.1|\n|---|---|---|---|\n|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)|\n|7B-Plus| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-plus-hf) | N/A | N/A |\n|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)|\n|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)|\n\n## Swallow Model Index NVE (No Vocabulary Expansion)\n|Model|Swallow-NVE-hf|Swallow-NVE-instruct-hf|\n|---|---|---|\n|7B| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-NVE-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-NVE-instruct-hf)|\n|13B| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-NVE-hf) | N/A |\n|70B| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-instruct-hf)|\n\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 LLaMA-2 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## Instruct Model Performance\n\n### MT-Bench JA\n\n\n#### Comparison to the past version\n\n* NOTE that the models with the `v0.1` suffix are newer versions compared to their original counterparts with the `hf`.\n* We report overall (i.e., average over scores of the first and second turns), first, and second turn scores.\n\n##### Overall\n\n\n|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|\n|---|---|---|---|---|---|---|---|---|---|\n| Swallow-7b-instruct-v0.1 |0.3435|0.4450|0.4720|0.1853|0.1920|0.2204|0.3015|0.4594|0.4720|\n| Swallow-7b-instruct-hf |0.1833|0.2205|0.1975|0.1593|0.1045|0.1282|0.2672|0.1908|0.1980|\n| Swallow-13b-instruct-v0.1 |0.3669|0.4816|0.5562|0.2769|0.1020|0.1505|0.4179|0.4347|0.5150|\n| Swallow-13b-instruct-hf |0.2004|0.1932|0.2552|0.1507|0.1184|0.1285|0.2641|0.2434|0.2500|\n| Swallow-70b-instruct-v0.1 |0.4513|0.4822|0.5353|0.3497|0.3492|0.2668|0.5553|0.4955|0.5767|\n| Swallow-70b-instruct-hf |0.3259|0.2925|0.4283|0.3447|0.1562|0.1856|0.5634|0.3315|0.3071|\n\n##### First Turn\n\n|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|\n|---|---|---|---|---|---|---|---|---|---|\n| Swallow-7b-instruct-v0.1 |0.3829|0.4960|0.4800|0.2220|0.2820|0.2164|0.3220|0.5440|0.4980|\n| Swallow-7b-instruct-hf |0.2216|0.2830|0.2150|0.1590|0.1080|0.1470|0.3542|0.2450|0.2650|\n| Swallow-13b-instruct-v0.1 |0.3948|0.5400|0.5220|0.3020|0.1040|0.1760|0.5040|0.5180|0.4920|\n| Swallow-13b-instruct-hf |0.2304|0.2460|0.2640|0.1610|0.1360|0.1330|0.3070|0.3010|0.2950|\n| Swallow-70b-instruct-v0.1 |0.4849|0.5720|0.5020|0.4780|0.3680|0.2467|0.5400|0.5720|0.5960|\n| Swallow-70b-instruct-hf |0.3631|0.3420|0.4007|0.4220|0.1580|0.2044|0.6120|0.4280|0.3360|\n\n##### Second Turn\n\n|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|\n|---|---|---|---|---|---|---|---|---|---|\n| Swallow-7b-instruct-v0.1 |0.3059|0.3940|0.4640|0.1441|0.1000|0.2253|0.2811|0.3724|0.4449|\n| Swallow-7b-instruct-hf |0.1432|0.1567|0.1798|0.1603|0.1010|0.1085|0.1767|0.1343|0.1295|\n| Swallow-13b-instruct-v0.1 |0.3353|0.4213|0.5911|0.2516|0.1000|0.1244|0.3194|0.3473|0.5394|\n| Swallow-13b-instruct-hf |0.1692|0.1364|0.2453|0.1401|0.1000|0.1237|0.2199|0.1850|0.2050|\n| Swallow-70b-instruct-v0.1 |0.4179|0.3913|0.5689|0.2184|0.3280|0.2884|0.5711|0.4171|0.5562|\n| Swallow-70b-instruct-hf |0.2872|0.2398|0.4564|0.2647|0.1540|0.1676|0.5118|0.2311|0.2762|\n\n#### Comparison to the existing models\n\nWe only provide the overall score in this section.\n\n##### 7B models\n\n|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|\n|---|---|---|---|---|---|---|---|---|---|\n| Swallow-7b-instruct-v0.1 |0.3435|0.4450|0.4720|0.1853|0.1920|0.2204|0.3015|0.4594|0.4720|\n| 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|\n| calm2-7b-chat |0.3204|0.4657|0.4898|0.1837|0.1005|0.1414|0.3927|0.3601|0.4293|\n| calm2-7b-chat-dpo-experimental |0.3493|0.5312|0.5237|0.1857|0.1000|0.1813|0.3355|0.4320|0.5051|\n| RakutenAI-7B-instruct |0.2994|0.3623|0.3711|0.3333|0.1763|0.1581|0.4215|0.2824|0.2901|\n| RakutenAI-7B-chat |0.3667|0.4229|0.4644|0.3990|0.2161|0.2390|0.3416|0.3904|0.4601|\n\n##### 13B models\n\n|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|\n|---|---|---|---|---|---|---|---|---|---|\n| Swallow-13b-instruct-v0.1 |0.3669|0.4816|0.5562|0.2769|0.1020|0.1505|0.4179|0.4347|0.5150|\n| ELYZA-japanese-Llama-2-13b-instruct |0.3196|0.4400|0.4373|0.2098|0.2157|0.1572|0.3583|0.3243|0.4141|\n| 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|\n\n##### 70B models\n\n|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|\n|---|---|---|---|---|---|---|---|---|---|\n| Swallow-70b-instruct-v0.1 |0.4513|0.4822|0.5353|0.3497|0.3492|0.2668|0.5553|0.4955|0.5767|\n| japanese-stablelm-instruct-beta-70b |0.3716|0.4179|0.3945|0.3656|0.2580|0.2186|0.4412|0.4663|0.4103|\n\n\n## Evaluation Benchmarks\n\n### MT-Bench JA\n\nWe used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the instruction-following capabilities of models.\nWe utilized the following settings:\n\n- Implemantation: FastChat [Zheng+, 2023] (commit #e86e70d0)\n- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)\n- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)\n- Prompt for Judge: [Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)\n- Judge: `gpt-4-1106-preview`\n- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.\n\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### Instruction format Ver0.1\nThis format must be adhered to strictly, as deviations may result in less optimal outputs from the model.\n\nThe template used to construct a prompt for the Instruct model is specified as follows:\n\n```\n<s>[INST] <<SYS>>\\n{SYSTEM_PROMPT}\\n<</SYS>>\\n\\n{USER_MESSAGE_1} [/INST] {BOT_MESSAGE_1}</s>[INST] {USER_MESSAGE_2} [/INST] \n```\n\n\nPlease 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.\n\nFor 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。\"\n\nFor the \"{USER_MESSAGE_1}\" part, We recommend using {instruction}\\n{input}\n\nIn other words, We recommend the following:\n\n``` \n<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] \n```\n\n\n### Use the instruct model Ver0.1\n\n```python\nimport torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nmodel_name = \"tokyotech-llm/Swallow-70b-instruct-v0.1\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map=\"auto\")\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\ndevice = \"cuda\"\n\nmessages = [\n    {\"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。\"},\n    {\"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\"}\n]\n\nencodeds = tokenizer.apply_chat_template(messages, return_tensors=\"pt\")\n\nmodel_inputs = encodeds.to(device)\nmodel.to(device)\n\ngenerated_ids = model.generate(model_inputs, max_new_tokens=128, do_sample=True)\ndecoded = tokenizer.batch_decode(generated_ids)\nprint(decoded[0])\n```\n\n## Training Datasets\n\n### Instruction Tuning Ver0.1\n\nThe following datasets were used for the instruction tuning. \n\n- [OpenAssistant Conversations Dataset EN top-1 thread](https://huggingface.co/datasets/OpenAssistant/oasst2)\n- [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.\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 Meta Research for releasing Llama 2 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\nLlama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.\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  - [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)\n  - [Ishida Shigeki](https://www.wantedly.com/id/reborn27)\n\n## How to cite\n```\n@misc{fujii2024continual,\n      title={Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities}, \n      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},\n      year={2024},\n      eprint={2404.17790},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n```\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2404.17790",
    "endpoints_compatible",
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