File size: 6,397 Bytes

---
library_name: transformers
tags:
- TensorBlock
- GGUF
base_model: Digest0703/DPO_model
---

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## Digest0703/DPO_model - GGUF

This repo contains GGUF format model files for [Digest0703/DPO_model](https://huggingface.co/Digest0703/DPO_model).

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

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 04 Dec 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [DPO_model-Q2_K.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q2_K.gguf) | Q2_K | 1.364 GB | smallest, significant quality loss - not recommended for most purposes |
| [DPO_model-Q3_K_S.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q3_K_S.gguf) | Q3_K_S | 1.543 GB | very small, high quality loss |
| [DPO_model-Q3_K_M.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q3_K_M.gguf) | Q3_K_M | 1.687 GB | very small, high quality loss |
| [DPO_model-Q3_K_L.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q3_K_L.gguf) | Q3_K_L | 1.815 GB | small, substantial quality loss |
| [DPO_model-Q4_0.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q4_0.gguf) | Q4_0 | 1.917 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [DPO_model-Q4_K_S.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q4_K_S.gguf) | Q4_K_S | 1.928 GB | small, greater quality loss |
| [DPO_model-Q4_K_M.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q4_K_M.gguf) | Q4_K_M | 2.019 GB | medium, balanced quality - recommended |
| [DPO_model-Q5_0.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q5_0.gguf) | Q5_0 | 2.270 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [DPO_model-Q5_K_S.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q5_K_S.gguf) | Q5_K_S | 2.270 GB | large, low quality loss - recommended |
| [DPO_model-Q5_K_M.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q5_K_M.gguf) | Q5_K_M | 2.322 GB | large, very low quality loss - recommended |
| [DPO_model-Q6_K.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q6_K.gguf) | Q6_K | 2.644 GB | very large, extremely low quality loss |
| [DPO_model-Q8_0.gguf](https://huggingface.co/tensorblock/DPO_model-GGUF/blob/main/DPO_model-Q8_0.gguf) | Q8_0 | 3.422 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/DPO_model-GGUF --include "DPO_model-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/DPO_model-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```