---
base_model: oscar128372/Qwen2.5-CoderX-7B-v0.5
tags:
- code-generation
- text-generation
- instruction-following
- fine-tuned
- qwen2
- unsloth
- transformers
- trl
- sft
- python
- physics-simulation
- algorithm-design
- TensorBlock
- GGUF
license: apache-2.0
language:
- en
---

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## oscar128372/Qwen2.5-CoderX-7B-v0.5 - GGUF

<div style="text-align: left; margin: 20px 0;">
    <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Join our Discord to learn more about what we're building ↗
    </a>
</div>

This repo contains GGUF format model files for [oscar128372/Qwen2.5-CoderX-7B-v0.5](https://huggingface.co/oscar128372/Qwen2.5-CoderX-7B-v0.5).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).

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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
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Qwen2.5-CoderX-7B-v0.5-Q2_K.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes |
| [Qwen2.5-CoderX-7B-v0.5-Q3_K_S.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss |
| [Qwen2.5-CoderX-7B-v0.5-Q3_K_M.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss |
| [Qwen2.5-CoderX-7B-v0.5-Q3_K_L.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss |
| [Qwen2.5-CoderX-7B-v0.5-Q4_0.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen2.5-CoderX-7B-v0.5-Q4_K_S.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss |
| [Qwen2.5-CoderX-7B-v0.5-Q4_K_M.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended |
| [Qwen2.5-CoderX-7B-v0.5-Q5_0.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen2.5-CoderX-7B-v0.5-Q5_K_S.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended |
| [Qwen2.5-CoderX-7B-v0.5-Q5_K_M.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended |
| [Qwen2.5-CoderX-7B-v0.5-Q6_K.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss |
| [Qwen2.5-CoderX-7B-v0.5-Q8_0.gguf](https://huggingface.co/tensorblock/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF/blob/main/Qwen2.5-CoderX-7B-v0.5-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/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF --include "Qwen2.5-CoderX-7B-v0.5-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/oscar128372_Qwen2.5-CoderX-7B-v0.5-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```