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JSL-MedLlama-3-8B-v1.0-GGUF

GGUF
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medical
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+ ---
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+ tags:
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+ - llama-3-8b
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+ - sft
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+ - medical
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+ - TensorBlock
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+ - GGUF
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+ base_model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
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+ license: cc-by-nc-nd-4.0
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+ ---
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+
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+ <div style="width: auto; margin-left: auto; margin-right: auto">
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+ <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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+ </div>
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+ <div style="display: flex; justify-content: space-between; width: 100%;">
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+ <div style="display: flex; flex-direction: column; align-items: flex-start;">
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+ <p style="margin-top: 0.5em; margin-bottom: 0em;">
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+ Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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+ </p>
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+ </div>
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+ </div>
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+
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+ ## johnsnowlabs/JSL-MedLlama-3-8B-v1.0 - GGUF
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+
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+ This repo contains GGUF format model files for [johnsnowlabs/JSL-MedLlama-3-8B-v1.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v1.0).
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+
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+ The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
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+
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+ ## Prompt template
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+
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+ ```
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+
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+ ```
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+
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+ ## Model file specification
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+
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+ | Filename | Quant type | File Size | Description |
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+ | -------- | ---------- | --------- | ----------- |
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+ | [JSL-MedLlama-3-8B-v1.0-Q2_K.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
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+ | [JSL-MedLlama-3-8B-v1.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
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+ | [JSL-MedLlama-3-8B-v1.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
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+ | [JSL-MedLlama-3-8B-v1.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
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+ | [JSL-MedLlama-3-8B-v1.0-Q4_0.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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+ | [JSL-MedLlama-3-8B-v1.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
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+ | [JSL-MedLlama-3-8B-v1.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
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+ | [JSL-MedLlama-3-8B-v1.0-Q5_0.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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+ | [JSL-MedLlama-3-8B-v1.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
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+ | [JSL-MedLlama-3-8B-v1.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
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+ | [JSL-MedLlama-3-8B-v1.0-Q6_K.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
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+ | [JSL-MedLlama-3-8B-v1.0-Q8_0.gguf](https://huggingface.co/tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF/tree/main/JSL-MedLlama-3-8B-v1.0-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended |
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+
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+
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+ ## Downloading instruction
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+
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+ ### Command line
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+
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+ Firstly, install Huggingface Client
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+
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+ ```shell
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+ pip install -U "huggingface_hub[cli]"
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+ ```
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+
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+ Then, downoad the individual model file the a local directory
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+
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+ ```shell
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+ huggingface-cli download tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF --include "JSL-MedLlama-3-8B-v1.0-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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+ ```
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+
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+ If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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+
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+ ```shell
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+ huggingface-cli download tensorblock/JSL-MedLlama-3-8B-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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+ ```