---
license: llama2
language:
- zh
- en
tags:
- TensorBlock
- GGUF
base_model: hpcai-tech/Colossal-LLaMA-2-7b-base
---

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## hpcai-tech/Colossal-LLaMA-2-7b-base - GGUF

This repo contains GGUF format model files for [hpcai-tech/Colossal-LLaMA-2-7b-base](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
  <tr>
    <th colspan="2" style="font-size: 25px;">Forge</th>
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    <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
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    <th colspan="2">
      <a href="https://github.com/TensorBlock/forge" target="_blank" style="
        display: inline-block;
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      ">🚀 Try it now! 🚀</a>
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  <tr>
    <th style="font-size: 25px;">Awesome MCP Servers</th>
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    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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      ">👀 See what we built 👀</a>
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        border-radius: 6px;
        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
    </th>
  </tr>
</table>

## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Colossal-LLaMA-2-7b-base-Q2_K.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q2_K.gguf) | Q2_K | 2.708 GB | smallest, significant quality loss - not recommended for most purposes |
| [Colossal-LLaMA-2-7b-base-Q3_K_S.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q3_K_S.gguf) | Q3_K_S | 3.139 GB | very small, high quality loss |
| [Colossal-LLaMA-2-7b-base-Q3_K_M.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q3_K_M.gguf) | Q3_K_M | 3.489 GB | very small, high quality loss |
| [Colossal-LLaMA-2-7b-base-Q3_K_L.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q3_K_L.gguf) | Q3_K_L | 3.788 GB | small, substantial quality loss |
| [Colossal-LLaMA-2-7b-base-Q4_0.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q4_0.gguf) | Q4_0 | 4.037 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Colossal-LLaMA-2-7b-base-Q4_K_S.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q4_K_S.gguf) | Q4_K_S | 4.068 GB | small, greater quality loss |
| [Colossal-LLaMA-2-7b-base-Q4_K_M.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q4_K_M.gguf) | Q4_K_M | 4.292 GB | medium, balanced quality - recommended |
| [Colossal-LLaMA-2-7b-base-Q5_0.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q5_0.gguf) | Q5_0 | 4.882 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Colossal-LLaMA-2-7b-base-Q5_K_S.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q5_K_S.gguf) | Q5_K_S | 4.882 GB | large, low quality loss - recommended |
| [Colossal-LLaMA-2-7b-base-Q5_K_M.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q5_K_M.gguf) | Q5_K_M | 5.013 GB | large, very low quality loss - recommended |
| [Colossal-LLaMA-2-7b-base-Q6_K.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q6_K.gguf) | Q6_K | 5.779 GB | very large, extremely low quality loss |
| [Colossal-LLaMA-2-7b-base-Q8_0.gguf](https://huggingface.co/tensorblock/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF/blob/main/Colossal-LLaMA-2-7b-base-Q8_0.gguf) | Q8_0 | 7.485 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/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF --include "Colossal-LLaMA-2-7b-base-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/hpcai-tech_Colossal-LLaMA-2-7b-base-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```