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OlympicCoder-7B-GGUF

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OlympicCoder-7B-GGUF
File size: 6,572 Bytes
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---
license: apache-2.0
datasets:
- open-r1/codeforces-cots
language:
- en
base_model: open-r1/OlympicCoder-7B
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
---

<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;">
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## open-r1/OlympicCoder-7B - GGUF

This repo contains GGUF format model files for [open-r1/OlympicCoder-7B](https://huggingface.co/open-r1/OlympicCoder-7B).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
  <tr>
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        display: inline-block;
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      ">🚀 Try it now! 🚀</a>
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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

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

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [OlympicCoder-7B-Q2_K.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes |
| [OlympicCoder-7B-Q3_K_S.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss |
| [OlympicCoder-7B-Q3_K_M.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss |
| [OlympicCoder-7B-Q3_K_L.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss |
| [OlympicCoder-7B-Q4_0.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [OlympicCoder-7B-Q4_K_S.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss |
| [OlympicCoder-7B-Q4_K_M.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended |
| [OlympicCoder-7B-Q5_0.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [OlympicCoder-7B-Q5_K_S.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended |
| [OlympicCoder-7B-Q5_K_M.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended |
| [OlympicCoder-7B-Q6_K.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss |
| [OlympicCoder-7B-Q8_0.gguf](https://huggingface.co/tensorblock/OlympicCoder-7B-GGUF/blob/main/OlympicCoder-7B-Q8_0.gguf) | Q8_0 | 8.099 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/OlympicCoder-7B-GGUF --include "OlympicCoder-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/OlympicCoder-7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```