richarderkhov/teilomillet_-_minimerlin-3b-v0.1-gguf overview

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    "summary": "Quantization made by Richard Erkhov. Github Discord Request more models MiniMerlin-3b-v0.1 - GGUF | Name | Quant method | Size | | ---- | ---- | ---- | | MiniMerlin-3b-v0.1.Q2_K.gguf | Q2_K | 1.09GB | | MiniMerlin-3b-v0.1.IQ3_XS.gguf | IQ3_XS | 1.21GB | | MiniMerlin-3b-v0.1.IQ3_S.gguf | IQ3_S | 1.27GB | | MiniMerlin-3b-v0.1.Q3_K_S.gguf | Q3_K_S | 1.27GB | | MiniMerlin-3b-v0.1.IQ3_M.gguf | IQ3_M | 1.33GB | | MiniMerlin-3b-v0.1.Q3_K.gguf | Q3_K | 1.4GB | | MiniMerlin-3b-v0.1.Q3_K_M.gguf | Q3_K_M | 1.4GB | | MiniMerlin-3b-v0.1.Q3_K_L.gguf | Q3_K_L | 1.52GB | | MiniMerlin-3b-v0.1.IQ4_XS.gguf | IQ4_XS | 1.55GB | | MiniMerlin-3b-v0.1.Q4_0.gguf | Q4_0 | 1.62GB | | MiniMerlin-3b-v0.1.IQ4_NL.gguf | IQ4_NL | 1.63GB | | MiniMerlin-3b-v0.1.Q4_K_S.gguf | Q4_K_S | 1.63GB | | MiniMerlin-3b-v0.1.Q4_K.gguf | Q4_K | 1.72GB | | MiniMerlin-3b-v0.1.Q4_K_M.gguf | Q4_K_M | 1.72GB | | MiniMerlin-3b-v0.1.Q4_1.gguf | Q4_1 | 1.79GB | | MiniMerlin-3b-v0.1.Q5_0.gguf | Q5_0 | 1.95GB | | MiniMerlin-3b-v0.1.Q5_K_S.gguf | Q5_K_S | 1.95GB | | MiniMerlin-3b-v0.1.Q5_K.gguf | Q5_K | 2.01GB | | MiniMerlin-3b-v0.1.Q5_K_M.gguf | Q5_K_M | 2.01GB | | MiniMerlin-3b-v0.1.Q5_1.gguf | Q5_1 | 2.12GB | | MiniMerlin-3b-v0.1.Q6_K.gguf | Q6_K | 2.31GB | | MiniMerlin-3b-v0.1.Q8_0.gguf | Q8_0 | 2.99GB | Original model description: --- license: apache-2.0 language: pipeline_tag: text-generation --- The aim was to test and try the dataset. Implementing a customization via a dataset and fine-tuning on it. The way to respond is also important to see if it's taken from the dataset and add to the customization. This is the first of a long serie of multiple models. Aimed to be minuscule as possible.",
    "quick_links": [],
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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\nMiniMerlin-3b-v0.1 - GGUF\n- Model creator: https://huggingface.co/teilomillet/\n- Original model: https://huggingface.co/teilomillet/MiniMerlin-3b-v0.1/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [MiniMerlin-3b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q2_K.gguf) | Q2_K | 1.09GB |\n| [MiniMerlin-3b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.IQ3_XS.gguf) | IQ3_XS | 1.21GB |\n| [MiniMerlin-3b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.IQ3_S.gguf) | IQ3_S | 1.27GB |\n| [MiniMerlin-3b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q3_K_S.gguf) | Q3_K_S | 1.27GB |\n| [MiniMerlin-3b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.IQ3_M.gguf) | IQ3_M | 1.33GB |\n| [MiniMerlin-3b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q3_K.gguf) | Q3_K | 1.4GB |\n| [MiniMerlin-3b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q3_K_M.gguf) | Q3_K_M | 1.4GB |\n| [MiniMerlin-3b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q3_K_L.gguf) | Q3_K_L | 1.52GB |\n| [MiniMerlin-3b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.IQ4_XS.gguf) | IQ4_XS | 1.55GB |\n| [MiniMerlin-3b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q4_0.gguf) | Q4_0 | 1.62GB |\n| [MiniMerlin-3b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.IQ4_NL.gguf) | IQ4_NL | 1.63GB |\n| [MiniMerlin-3b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q4_K_S.gguf) | Q4_K_S | 1.63GB |\n| [MiniMerlin-3b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q4_K.gguf) | Q4_K | 1.72GB |\n| [MiniMerlin-3b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q4_K_M.gguf) | Q4_K_M | 1.72GB |\n| [MiniMerlin-3b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q4_1.gguf) | Q4_1 | 1.79GB |\n| [MiniMerlin-3b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q5_0.gguf) | Q5_0 | 1.95GB |\n| [MiniMerlin-3b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q5_K_S.gguf) | Q5_K_S | 1.95GB |\n| [MiniMerlin-3b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q5_K.gguf) | Q5_K | 2.01GB |\n| [MiniMerlin-3b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q5_K_M.gguf) | Q5_K_M | 2.01GB |\n| [MiniMerlin-3b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q5_1.gguf) | Q5_1 | 2.12GB |\n| [MiniMerlin-3b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q6_K.gguf) | Q6_K | 2.31GB |\n| [MiniMerlin-3b-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3b-v0.1-gguf/blob/main/MiniMerlin-3b-v0.1.Q8_0.gguf) | Q8_0 | 2.99GB |\n\n\n\n\nOriginal model description:\n---\nlicense: apache-2.0\nlanguage:\n- fr\npipeline_tag: text-generation\n---\n\n- Model : https://huggingface.co/GeneZC/MiniChat-1.5-3B\n- FT : @teilomillet\n\n- Instruction tune using QLoRA on a french dataset for 1 epoch.\nThe aim was to test and try the dataset. Implementing a customization via a dataset and fine-tuning on it. \nThe way to respond is also important to see if it's taken from the dataset and add to the customization.\n\nThis is the first of a long serie of multiple models. Aimed to be minuscule as possible.\n\n- Batch : 6\n- Gradient step : 1\n- Epoch : 1\n- Lr : 0.0002\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "endpoints_compatible",
    "region:us"
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  "last_modified": "2024-07-31T05:51:49.000Z",
  "created_at": "2024-07-31T05:03:57.000Z",
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