metadata
license: apache-2.0
datasets: We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs
language:
- ko
pipeline_tag: text-generation
base_model: dddsaty/KoSOLAR-10.7B_DPO_Adapter_Attach
tags:
- TensorBlock
- GGUF
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
dddsaty/KoSOLAR-10.7B_DPO_Adapter_Attach - GGUF
This repo contains GGUF format model files for dddsaty/KoSOLAR-10.7B_DPO_Adapter_Attach.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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 |
|---|---|---|---|
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q2_K.gguf | Q2_K | 3.768 GB | smallest, significant quality loss - not recommended for most purposes |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q3_K_S.gguf | Q3_K_S | 4.387 GB | very small, high quality loss |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q3_K_M.gguf | Q3_K_M | 4.882 GB | very small, high quality loss |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q3_K_L.gguf | Q3_K_L | 5.306 GB | small, substantial quality loss |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q4_0.gguf | Q4_0 | 5.703 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q4_K_S.gguf | Q4_K_S | 5.746 GB | small, greater quality loss |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q4_K_M.gguf | Q4_K_M | 6.065 GB | medium, balanced quality - recommended |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q5_0.gguf | Q5_0 | 6.941 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q5_K_S.gguf | Q5_K_S | 6.941 GB | large, low quality loss - recommended |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q5_K_M.gguf | Q5_K_M | 7.128 GB | large, very low quality loss - recommended |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q6_K.gguf | Q6_K | 8.257 GB | very large, extremely low quality loss |
| KoSOLAR-10.7B_DPO_Adapter_Attach-Q8_0.gguf | Q8_0 | 10.694 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/KoSOLAR-10.7B_DPO_Adapter_Attach-GGUF --include "KoSOLAR-10.7B_DPO_Adapter_Attach-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/KoSOLAR-10.7B_DPO_Adapter_Attach-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'