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---
base_model: inswave/AISquare-Instruct-llama2-koen-13b-v0.9.5
tags:
- TensorBlock
- GGUF
---
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## inswave/AISquare-Instruct-llama2-koen-13b-v0.9.5 - GGUF
This repo contains GGUF format model files for [inswave/AISquare-Instruct-llama2-koen-13b-v0.9.5](https://huggingface.co/inswave/AISquare-Instruct-llama2-koen-13b-v0.9.5).
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
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q2_K.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q2_K.gguf) | Q2_K | 4.600 GB | smallest, significant quality loss - not recommended for most purposes |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q3_K_S.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q3_K_S.gguf) | Q3_K_S | 5.356 GB | very small, high quality loss |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q3_K_M.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q3_K_M.gguf) | Q3_K_M | 5.988 GB | very small, high quality loss |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q3_K_L.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q3_K_L.gguf) | Q3_K_L | 6.539 GB | small, substantial quality loss |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q4_0.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q4_0.gguf) | Q4_0 | 6.955 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q4_K_S.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q4_K_S.gguf) | Q4_K_S | 7.008 GB | small, greater quality loss |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q4_K_M.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q4_K_M.gguf) | Q4_K_M | 7.421 GB | medium, balanced quality - recommended |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q5_0.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q5_0.gguf) | Q5_0 | 8.459 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q5_K_S.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q5_K_S.gguf) | Q5_K_S | 8.459 GB | large, low quality loss - recommended |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q5_K_M.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q5_K_M.gguf) | Q5_K_M | 8.699 GB | large, very low quality loss - recommended |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q6_K.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q6_K.gguf) | Q6_K | 10.058 GB | very large, extremely low quality loss |
| [AISquare-Instruct-llama2-koen-13b-v0.9.5-Q8_0.gguf](https://huggingface.co/tensorblock/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF/tree/main/AISquare-Instruct-llama2-koen-13b-v0.9.5-Q8_0.gguf) | Q8_0 | 13.027 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/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF --include "AISquare-Instruct-llama2-koen-13b-v0.9.5-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/AISquare-Instruct-llama2-koen-13b-v0.9.5-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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