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MD-Judge-v0_2-internlm2_7b-GGUF

Text Generation
GGUF
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MD-Judge-v0_2-internlm2_7b-GGUF
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---
license: apache-2.0
datasets:
- lmsys/lmsys-chat-1m
- PKU-Alignment/BeaverTails
- allenai/WildChat-1M
language:
- en
metrics:
- f1
- accuracy
tags:
- ai-safety
- safetyguard
- safety
- benchmark
- internlm
- evaluation
- judge
- TensorBlock
- GGUF
pipeline_tag: text-generation
base_model: OpenSafetyLab/MD-Judge-v0_2-internlm2_7b
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</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>
        </p>
    </div>
</div>

## OpenSafetyLab/MD-Judge-v0_2-internlm2_7b - GGUF

This repo contains GGUF format model files for [OpenSafetyLab/MD-Judge-v0_2-internlm2_7b](https://huggingface.co/OpenSafetyLab/MD-Judge-v0_2-internlm2_7b).

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).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
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  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
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    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
      display: inline-block;
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    ">👀 See what we built 👀</a>
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</table>
## Prompt template

```
{system_prompt}<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [MD-Judge-v0_2-internlm2_7b-Q2_K.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q2_K.gguf) | Q2_K | 3.005 GB | smallest, significant quality loss - not recommended for most purposes |
| [MD-Judge-v0_2-internlm2_7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q3_K_S.gguf) | Q3_K_S | 3.476 GB | very small, high quality loss |
| [MD-Judge-v0_2-internlm2_7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q3_K_M.gguf) | Q3_K_M | 3.830 GB | very small, high quality loss |
| [MD-Judge-v0_2-internlm2_7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q3_K_L.gguf) | Q3_K_L | 4.133 GB | small, substantial quality loss |
| [MD-Judge-v0_2-internlm2_7b-Q4_0.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q4_0.gguf) | Q4_0 | 4.453 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [MD-Judge-v0_2-internlm2_7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q4_K_S.gguf) | Q4_K_S | 4.485 GB | small, greater quality loss |
| [MD-Judge-v0_2-internlm2_7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q4_K_M.gguf) | Q4_K_M | 4.713 GB | medium, balanced quality - recommended |
| [MD-Judge-v0_2-internlm2_7b-Q5_0.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q5_0.gguf) | Q5_0 | 5.373 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [MD-Judge-v0_2-internlm2_7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q5_K_S.gguf) | Q5_K_S | 5.373 GB | large, low quality loss - recommended |
| [MD-Judge-v0_2-internlm2_7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q5_K_M.gguf) | Q5_K_M | 5.507 GB | large, very low quality loss - recommended |
| [MD-Judge-v0_2-internlm2_7b-Q6_K.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q6_K.gguf) | Q6_K | 6.350 GB | very large, extremely low quality loss |
| [MD-Judge-v0_2-internlm2_7b-Q8_0.gguf](https://huggingface.co/tensorblock/MD-Judge-v0_2-internlm2_7b-GGUF/blob/main/MD-Judge-v0_2-internlm2_7b-Q8_0.gguf) | Q8_0 | 8.224 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/MD-Judge-v0_2-internlm2_7b-GGUF --include "MD-Judge-v0_2-internlm2_7b-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/MD-Judge-v0_2-internlm2_7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```