---
license: apache-2.0
tags:
- generated_from_trainer
- TensorBlock
- GGUF
base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
model-index:
- name: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
  results: []
---

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## yanolja/EEVE-Korean-Instruct-10.8B-v1.0 - GGUF

This repo contains GGUF format model files for [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co./yanolja/EEVE-Korean-Instruct-10.8B-v1.0).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

## 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 |
| -------- | ---------- | --------- | ----------- |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q2_K.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q2_K.gguf) | Q2_K | 3.768 GB | smallest, significant quality loss - not recommended for most purposes |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q3_K_S.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q3_K_S.gguf) | Q3_K_S | 4.387 GB | very small, high quality loss |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q3_K_M.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q3_K_M.gguf) | Q3_K_M | 4.882 GB | very small, high quality loss |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q3_K_L.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q3_K_L.gguf) | Q3_K_L | 5.306 GB | small, substantial quality loss |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q4_0.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q4_0.gguf) | Q4_0 | 5.703 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q4_K_S.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q4_K_S.gguf) | Q4_K_S | 5.746 GB | small, greater quality loss |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q4_K_M.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q4_K_M.gguf) | Q4_K_M | 6.065 GB | medium, balanced quality - recommended |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q5_0.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q5_0.gguf) | Q5_0 | 6.941 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q5_K_S.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q5_K_S.gguf) | Q5_K_S | 6.941 GB | large, low quality loss - recommended |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q5_K_M.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q5_K_M.gguf) | Q5_K_M | 7.128 GB | large, very low quality loss - recommended |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q6_K.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q6_K.gguf) | Q6_K | 8.257 GB | very large, extremely low quality loss |
| [EEVE-Korean-Instruct-10.8B-v1.0-Q8_0.gguf](https://huggingface.co./tensorblock/EEVE-Korean-Instruct-10.8B-v1.0-GGUF/tree/main/EEVE-Korean-Instruct-10.8B-v1.0-Q8_0.gguf) | Q8_0 | 10.694 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/EEVE-Korean-Instruct-10.8B-v1.0-GGUF --include "EEVE-Korean-Instruct-10.8B-v1.0-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/EEVE-Korean-Instruct-10.8B-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```