---
license: apache-2.0
language:
- zh
- en
base_model: AiCloser/Qwen2.5-32B-AGI
datasets:
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- unalignment/toxic-dpo-v0.2
- Orion-zhen/dpo-toxic-zh
library_name: transformers
tags:
- TensorBlock
- GGUF
---

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## AiCloser/Qwen2.5-32B-AGI - GGUF

This repo contains GGUF format model files for [AiCloser/Qwen2.5-32B-AGI](https://huggingface.co/AiCloser/Qwen2.5-32B-AGI).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

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## 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 |
| -------- | ---------- | --------- | ----------- |
| [Qwen2.5-32B-AGI-Q2_K.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q2_K.gguf) | Q2_K | 12.313 GB | smallest, significant quality loss - not recommended for most purposes |
| [Qwen2.5-32B-AGI-Q3_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q3_K_S.gguf) | Q3_K_S | 14.392 GB | very small, high quality loss |
| [Qwen2.5-32B-AGI-Q3_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q3_K_M.gguf) | Q3_K_M | 15.935 GB | very small, high quality loss |
| [Qwen2.5-32B-AGI-Q3_K_L.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q3_K_L.gguf) | Q3_K_L | 17.247 GB | small, substantial quality loss |
| [Qwen2.5-32B-AGI-Q4_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q4_0.gguf) | Q4_0 | 18.640 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen2.5-32B-AGI-Q4_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q4_K_S.gguf) | Q4_K_S | 18.784 GB | small, greater quality loss |
| [Qwen2.5-32B-AGI-Q4_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q4_K_M.gguf) | Q4_K_M | 19.851 GB | medium, balanced quality - recommended |
| [Qwen2.5-32B-AGI-Q5_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q5_0.gguf) | Q5_0 | 22.638 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen2.5-32B-AGI-Q5_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q5_K_S.gguf) | Q5_K_S | 22.638 GB | large, low quality loss - recommended |
| [Qwen2.5-32B-AGI-Q5_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q5_K_M.gguf) | Q5_K_M | 23.262 GB | large, very low quality loss - recommended |
| [Qwen2.5-32B-AGI-Q6_K.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q6_K.gguf) | Q6_K | 26.886 GB | very large, extremely low quality loss |
| [Qwen2.5-32B-AGI-Q8_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-32B-AGI-GGUF/blob/main/Qwen2.5-32B-AGI-Q8_0.gguf) | Q8_0 | 34.821 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/Qwen2.5-32B-AGI-GGUF --include "Qwen2.5-32B-AGI-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/Qwen2.5-32B-AGI-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```