---
tags:
- merge
- mergekit
- lazymergekit
- winninghealth/WiNGPT2-Llama-3-8B-Base
- johnsnowlabs/JSL-MedLlama-3-8B-v1.0
- TensorBlock
- GGUF
base_model: shanchen/llama3-8B-slerp-med-chinese
license: llama3
language:
- zh
- en
- fr
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            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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## shanchen/llama3-8B-slerp-med-chinese - GGUF

This repo contains GGUF format model files for [shanchen/llama3-8B-slerp-med-chinese](https://huggingface.co/shanchen/llama3-8B-slerp-med-chinese).

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).

## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama3-8B-slerp-med-chinese-Q2_K.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama3-8B-slerp-med-chinese-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
| [llama3-8B-slerp-med-chinese-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
| [llama3-8B-slerp-med-chinese-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
| [llama3-8B-slerp-med-chinese-Q4_0.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama3-8B-slerp-med-chinese-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
| [llama3-8B-slerp-med-chinese-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
| [llama3-8B-slerp-med-chinese-Q5_0.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama3-8B-slerp-med-chinese-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
| [llama3-8B-slerp-med-chinese-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
| [llama3-8B-slerp-med-chinese-Q6_K.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
| [llama3-8B-slerp-med-chinese-Q8_0.gguf](https://huggingface.co/tensorblock/llama3-8B-slerp-med-chinese-GGUF/tree/main/llama3-8B-slerp-med-chinese-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/llama3-8B-slerp-med-chinese-GGUF --include "llama3-8B-slerp-med-chinese-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/llama3-8B-slerp-med-chinese-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```