richarderkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf overview

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    "hero_image_url": "https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png",
    "summary": "We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing",
    "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\nmistral-7b-instruct-v0.3 - GGUF\n- Model creator: https://huggingface.co/unsloth/\n- Original model: https://huggingface.co/unsloth/mistral-7b-instruct-v0.3/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [mistral-7b-instruct-v0.3.Q2_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q2_K.gguf) | Q2_K | 2.54GB |\n| [mistral-7b-instruct-v0.3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.IQ3_XS.gguf) | IQ3_XS | 2.82GB |\n| [mistral-7b-instruct-v0.3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.IQ3_S.gguf) | IQ3_S | 2.97GB |\n| [mistral-7b-instruct-v0.3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q3_K_S.gguf) | Q3_K_S | 2.95GB |\n| [mistral-7b-instruct-v0.3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.IQ3_M.gguf) | IQ3_M | 3.06GB |\n| [mistral-7b-instruct-v0.3.Q3_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q3_K.gguf) | Q3_K | 3.28GB |\n| [mistral-7b-instruct-v0.3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q3_K_M.gguf) | Q3_K_M | 3.28GB |\n| [mistral-7b-instruct-v0.3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q3_K_L.gguf) | Q3_K_L | 3.56GB |\n| [mistral-7b-instruct-v0.3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.IQ4_XS.gguf) | IQ4_XS | 3.68GB |\n| [mistral-7b-instruct-v0.3.Q4_0.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q4_0.gguf) | Q4_0 | 3.83GB |\n| [mistral-7b-instruct-v0.3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.IQ4_NL.gguf) | IQ4_NL | 3.87GB |\n| [mistral-7b-instruct-v0.3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q4_K_S.gguf) | Q4_K_S | 3.86GB |\n| [mistral-7b-instruct-v0.3.Q4_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q4_K.gguf) | Q4_K | 4.07GB |\n| [mistral-7b-instruct-v0.3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q4_K_M.gguf) | Q4_K_M | 4.07GB |\n| [mistral-7b-instruct-v0.3.Q4_1.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q4_1.gguf) | Q4_1 | 4.24GB |\n| [mistral-7b-instruct-v0.3.Q5_0.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q5_0.gguf) | Q5_0 | 4.66GB |\n| [mistral-7b-instruct-v0.3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q5_K_S.gguf) | Q5_K_S | 4.66GB |\n| [mistral-7b-instruct-v0.3.Q5_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q5_K.gguf) | Q5_K | 4.78GB |\n| [mistral-7b-instruct-v0.3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q5_K_M.gguf) | Q5_K_M | 4.78GB |\n| [mistral-7b-instruct-v0.3.Q5_1.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q5_1.gguf) | Q5_1 | 5.07GB |\n| [mistral-7b-instruct-v0.3.Q6_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q6_K.gguf) | Q6_K | 5.54GB |\n| [mistral-7b-instruct-v0.3.Q8_0.gguf](https://huggingface.co/RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf/blob/main/mistral-7b-instruct-v0.3.Q8_0.gguf) | Q8_0 | 7.17GB |\n\n\n\n\nOriginal model description:\n---\nlanguage:\n- en\nlicense: apache-2.0\nlibrary_name: transformers\ntags:\n- unsloth\n- transformers\n- mistral\n- mistral-7b\n- mistral-instruct\n- instruct\n\n---\n\n# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!\n\nWe have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing\n\nFor conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing\n\n[<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png\" width=\"200\"/>](https://discord.gg/u54VK8m8tk)\n[<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png\" width=\"200\"/>](https://ko-fi.com/unsloth)\n[<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png\" width=\"200\"/>](https://github.com/unslothai/unsloth)\n\n## ✨ Finetune for Free\n\nAll notebooks are **beginner friendly**! Add your dataset, click \"Run All\", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.\n\n| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |\n|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|\n| **Gemma 7b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)               | 2.4x faster | 58% less |\n| **Mistral 7b**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |\n| **Llama-2 7b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)               | 2.2x faster | 43% less |\n| **TinyLlama**  | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)              | 3.9x faster | 74% less |\n| **CodeLlama 34b** A100   | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)              | 1.9x faster | 27% less |\n| **Mistral 7b** 1xT4  | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\\* | 62% less |\n| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |\n\n- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.\n- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.\n- \\* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.\n\n\n",
    "related_quantizations": []
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    "endpoints_compatible",
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