metadata
language:
- ko
tags:
- llama-2
- instruct
- instruction
- TensorBlock
- GGUF
pipeline_tag: text-generation
license: llama2
datasets:
- squarelike/OpenOrca-gugugo-ko
base_model: daekeun-ml/Llama-2-ko-OpenOrca-gugugo-13B
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
daekeun-ml/Llama-2-ko-OpenOrca-gugugo-13B - GGUF
This repo contains GGUF format model files for daekeun-ml/Llama-2-ko-OpenOrca-gugugo-13B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Llama-2-ko-OpenOrca-gugugo-13B-Q2_K.gguf | Q2_K | 4.600 GB | smallest, significant quality loss - not recommended for most purposes |
| Llama-2-ko-OpenOrca-gugugo-13B-Q3_K_S.gguf | Q3_K_S | 5.356 GB | very small, high quality loss |
| Llama-2-ko-OpenOrca-gugugo-13B-Q3_K_M.gguf | Q3_K_M | 5.988 GB | very small, high quality loss |
| Llama-2-ko-OpenOrca-gugugo-13B-Q3_K_L.gguf | Q3_K_L | 6.539 GB | small, substantial quality loss |
| Llama-2-ko-OpenOrca-gugugo-13B-Q4_0.gguf | Q4_0 | 6.955 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Llama-2-ko-OpenOrca-gugugo-13B-Q4_K_S.gguf | Q4_K_S | 7.008 GB | small, greater quality loss |
| Llama-2-ko-OpenOrca-gugugo-13B-Q4_K_M.gguf | Q4_K_M | 7.421 GB | medium, balanced quality - recommended |
| Llama-2-ko-OpenOrca-gugugo-13B-Q5_0.gguf | Q5_0 | 8.459 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Llama-2-ko-OpenOrca-gugugo-13B-Q5_K_S.gguf | Q5_K_S | 8.459 GB | large, low quality loss - recommended |
| Llama-2-ko-OpenOrca-gugugo-13B-Q5_K_M.gguf | Q5_K_M | 8.699 GB | large, very low quality loss - recommended |
| Llama-2-ko-OpenOrca-gugugo-13B-Q6_K.gguf | Q6_K | 10.058 GB | very large, extremely low quality loss |
| Llama-2-ko-OpenOrca-gugugo-13B-Q8_0.gguf | Q8_0 | 13.027 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/Llama-2-ko-OpenOrca-gugugo-13B-GGUF --include "Llama-2-ko-OpenOrca-gugugo-13B-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:
huggingface-cli download tensorblock/Llama-2-ko-OpenOrca-gugugo-13B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'