richarderkhov/yentinglin_-_taiwan-llm-7b-v2.0-base-gguf overview

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    "summary": "# Model Card for Taiwan LLM 7B v2.0 base Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. For detailed insights into Taiwan LLM's development and features, refer to our technical report.",
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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\nTaiwan-LLM-7B-v2.0-base - GGUF\n- Model creator: https://huggingface.co/yentinglin/\n- Original model: https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.0-base/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [Taiwan-LLM-7B-v2.0-base.Q2_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q2_K.gguf) | Q2_K | 2.36GB |\n| [Taiwan-LLM-7B-v2.0-base.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_XS.gguf) | IQ3_XS | 2.6GB |\n| [Taiwan-LLM-7B-v2.0-base.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_S.gguf) | IQ3_S | 2.75GB |\n| [Taiwan-LLM-7B-v2.0-base.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_S.gguf) | Q3_K_S | 2.75GB |\n| [Taiwan-LLM-7B-v2.0-base.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_M.gguf) | IQ3_M | 2.9GB |\n| [Taiwan-LLM-7B-v2.0-base.Q3_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K.gguf) | Q3_K | 3.07GB |\n| [Taiwan-LLM-7B-v2.0-base.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_M.gguf) | Q3_K_M | 3.07GB |\n| [Taiwan-LLM-7B-v2.0-base.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_L.gguf) | Q3_K_L | 3.35GB |\n| [Taiwan-LLM-7B-v2.0-base.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ4_XS.gguf) | IQ4_XS | 3.4GB |\n| [Taiwan-LLM-7B-v2.0-base.Q4_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_0.gguf) | Q4_0 | 3.56GB |\n| [Taiwan-LLM-7B-v2.0-base.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ4_NL.gguf) | IQ4_NL | 3.58GB |\n| [Taiwan-LLM-7B-v2.0-base.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K_S.gguf) | Q4_K_S | 3.59GB |\n| [Taiwan-LLM-7B-v2.0-base.Q4_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K.gguf) | Q4_K | 3.8GB |\n| [Taiwan-LLM-7B-v2.0-base.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K_M.gguf) | Q4_K_M | 3.8GB |\n| [Taiwan-LLM-7B-v2.0-base.Q4_1.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_1.gguf) | Q4_1 | 3.95GB |\n| [Taiwan-LLM-7B-v2.0-base.Q5_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_0.gguf) | Q5_0 | 4.33GB |\n| [Taiwan-LLM-7B-v2.0-base.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K_S.gguf) | Q5_K_S | 4.33GB |\n| [Taiwan-LLM-7B-v2.0-base.Q5_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K.gguf) | Q5_K | 4.45GB |\n| [Taiwan-LLM-7B-v2.0-base.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K_M.gguf) | Q5_K_M | 4.45GB |\n| [Taiwan-LLM-7B-v2.0-base.Q5_1.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_1.gguf) | Q5_1 | 4.72GB |\n| [Taiwan-LLM-7B-v2.0-base.Q6_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q6_K.gguf) | Q6_K | 5.15GB |\n| [Taiwan-LLM-7B-v2.0-base.Q8_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q8_0.gguf) | Q8_0 | 6.67GB |\n\n\n\n\nOriginal model description:\n---\nlicense: apache-2.0\nlanguage:\n- zh\nwidget:\n - text: \"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:\"\nlibrary_name: transformers\npipeline_tag: text-generation\nextra_gated_heading: Acknowledge license to accept the repository.\nextra_gated_prompt: Please contact the author for access.\nextra_gated_button_content: Acknowledge license 同意以上內容\nextra_gated_fields:\n  Name: text\n  Mail: text\n  Organization: text\n  Country: text\n  Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox\n  使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox\n---\n<img src=\"https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png\" alt=\"Taiwan LLM Logo\" width=\"800\" style=\"margin-left:'auto' margin-right:'auto' display:'block'\"/>\n\n# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟\n\n# Model Card for Taiwan LLM 7B v2.0 base\n\nTaiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. \nDeveloped from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. \nThis model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. \nIt demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. \nFor detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf).\n\n\n## Model description\n\n- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.\n- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw)\n- **Finetuned from model:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/yentinglin/meta-llama/Llama-2-7b-hf)\n\n### Model Sources\n\n<!-- Provide the basic links for the model. -->\n\n- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa\n- **Demo:** https://twllm.com/\n\n## Performance\n\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/HTwIzw6RDha2-PhuWqSuI.png)\n\n## Intended uses\n\nYou should fine-tuned this model for instruction-following / chat application.\n\n### Training hyperparameters\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/MdvHwdUvH-c926qyRAw7K.png)\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/kKpkvxDzOEyiAoTqmzRYO.png)\n\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/FsnlJ_fkRxf7fn5RKZnjE.png)\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- distributed_type: multi-GPU\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 5.0\n\n## Citation\n\nIf you find Taiwan LLM is useful in your work, please cite it with:\n\n```\n@misc{lin2023taiwan,\n      title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model}, \n      author={Yen-Ting Lin and Yun-Nung Chen},\n      year={2023},\n      eprint={2311.17487},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n```\n\n# Acknowledgement\n\nTaiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.\n\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2311.17487",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "last_modified": "2024-05-30T06:04:06.000Z",
  "created_at": "2024-05-30T03:19:50.000Z",
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