richarderkhov/minillm_-_miniplm-qwen-500m-gguf overview

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    "summary": "paper | code **MiniPLM-Qwen-500M** is a 500M model with Qwen achitecture pre-trained from scratch on the Pile using the MiniPLM knowledge distillation framework with the offcial Qwen1.5-1.8B as the teacher model. We also open-source the pre-training corpus refined by Difference Sampling in MiniPLM for reproducibility.",
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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\nMiniPLM-Qwen-500M - GGUF\n- Model creator: https://huggingface.co/MiniLLM/\n- Original model: https://huggingface.co/MiniLLM/MiniPLM-Qwen-500M/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [MiniPLM-Qwen-500M.Q2_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q2_K.gguf) | Q2_K | 0.23GB |\n| [MiniPLM-Qwen-500M.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q3_K_S.gguf) | Q3_K_S | 0.25GB |\n| [MiniPLM-Qwen-500M.Q3_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q3_K.gguf) | Q3_K | 0.26GB |\n| [MiniPLM-Qwen-500M.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q3_K_M.gguf) | Q3_K_M | 0.26GB |\n| [MiniPLM-Qwen-500M.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q3_K_L.gguf) | Q3_K_L | 0.28GB |\n| [MiniPLM-Qwen-500M.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.IQ4_XS.gguf) | IQ4_XS | 0.28GB |\n| [MiniPLM-Qwen-500M.Q4_0.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q4_0.gguf) | Q4_0 | 0.29GB |\n| [MiniPLM-Qwen-500M.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.IQ4_NL.gguf) | IQ4_NL | 0.29GB |\n| [MiniPLM-Qwen-500M.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q4_K_S.gguf) | Q4_K_S | 0.29GB |\n| [MiniPLM-Qwen-500M.Q4_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q4_K.gguf) | Q4_K | 0.3GB |\n| [MiniPLM-Qwen-500M.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q4_K_M.gguf) | Q4_K_M | 0.3GB |\n| [MiniPLM-Qwen-500M.Q4_1.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q4_1.gguf) | Q4_1 | 0.3GB |\n| [MiniPLM-Qwen-500M.Q5_0.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q5_0.gguf) | Q5_0 | 0.32GB |\n| [MiniPLM-Qwen-500M.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q5_K_S.gguf) | Q5_K_S | 0.32GB |\n| [MiniPLM-Qwen-500M.Q5_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q5_K.gguf) | Q5_K | 0.33GB |\n| [MiniPLM-Qwen-500M.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q5_K_M.gguf) | Q5_K_M | 0.33GB |\n| [MiniPLM-Qwen-500M.Q5_1.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q5_1.gguf) | Q5_1 | 0.34GB |\n| [MiniPLM-Qwen-500M.Q6_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q6_K.gguf) | Q6_K | 0.36GB |\n| [MiniPLM-Qwen-500M.Q8_0.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_MiniPLM-Qwen-500M-gguf/blob/main/MiniPLM-Qwen-500M.Q8_0.gguf) | Q8_0 | 0.47GB |\n\n\n\n\nOriginal model description:\n---\nlibrary_name: transformers\nlicense: apache-2.0\ndatasets:\n- monology/pile-uncopyrighted\n- MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5\nlanguage:\n- en\nmetrics:\n- accuracy\npipeline_tag: text-generation\n---\n\n# MinPLM-Qwen-500M\n\n[paper](https://arxiv.org/abs/2410.17215) | [code](https://github.com/thu-coai/MiniPLM)\n\n**MiniPLM-Qwen-500M** is a 500M model with Qwen achitecture pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) using the MiniPLM knowledge distillation framework with the [offcial Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) as the teacher model.\n\nWe also open-source the [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5) refined by Difference Sampling in MiniPLM for reproducibility.\n\n<p align='left'>\n    <img src=\"https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/2BqT0NgkmIXYlktovw9kG.png\" width=\"1000\">\n</p>\n\n## Evaluation\n\nMiniPLM models achieves better performance given the same computation and scales well across model sizes:\n\n<p align='left'>\n    <img src=\"https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/EOYzajQcwQFT5PobqL3j0.png\" width=\"1000\">\n</p>\n\n## Baseline Models\n+ [Conventional Pre-Training](https://huggingface.co/MiniLLM/Pretrain-Qwen-500M)\n+ [VanillaKD](https://huggingface.co/MiniLLM/VanillaKD-Pretrain-Qwen-500M)\n\n## Citation\n\n```bibtex\n@article{miniplm,\n    title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, \n    author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},\n    journal={arXiv preprint arXiv:2410.17215},\n    year={2024}\n}\n```\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2410.17215",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "last_modified": "2024-11-03T10:30:36.000Z",
  "created_at": "2024-11-03T10:26:03.000Z",
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