metadata
datasets:
- c-s-ale/alpaca-gpt4-data
- Open-Orca/OpenOrca
- Intel/orca_dpo_pairs
- allenai/ultrafeedback_binarized_cleaned
language:
- en
license: cc-by-nc-4.0
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
tags:
- TensorBlock
- GGUF
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
upstage/SOLAR-10.7B-Instruct-v1.0 - GGUF
This repo contains GGUF format model files for upstage/SOLAR-10.7B-Instruct-v1.0.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
### System:
{system_prompt}
### User:
{prompt}
### Assistant:
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| SOLAR-10.7B-Instruct-v1.0-Q2_K.gguf | Q2_K | 3.728 GB | smallest, significant quality loss - not recommended for most purposes |
| SOLAR-10.7B-Instruct-v1.0-Q3_K_S.gguf | Q3_K_S | 4.344 GB | very small, high quality loss |
| SOLAR-10.7B-Instruct-v1.0-Q3_K_M.gguf | Q3_K_M | 4.839 GB | very small, high quality loss |
| SOLAR-10.7B-Instruct-v1.0-Q3_K_L.gguf | Q3_K_L | 5.263 GB | small, substantial quality loss |
| SOLAR-10.7B-Instruct-v1.0-Q4_0.gguf | Q4_0 | 5.655 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| SOLAR-10.7B-Instruct-v1.0-Q4_K_S.gguf | Q4_K_S | 5.698 GB | small, greater quality loss |
| SOLAR-10.7B-Instruct-v1.0-Q4_K_M.gguf | Q4_K_M | 6.018 GB | medium, balanced quality - recommended |
| SOLAR-10.7B-Instruct-v1.0-Q5_0.gguf | Q5_0 | 6.889 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| SOLAR-10.7B-Instruct-v1.0-Q5_K_S.gguf | Q5_K_S | 6.889 GB | large, low quality loss - recommended |
| SOLAR-10.7B-Instruct-v1.0-Q5_K_M.gguf | Q5_K_M | 7.076 GB | large, very low quality loss - recommended |
| SOLAR-10.7B-Instruct-v1.0-Q6_K.gguf | Q6_K | 8.200 GB | very large, extremely low quality loss |
| SOLAR-10.7B-Instruct-v1.0-Q8_0.gguf | Q8_0 | 10.621 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/SOLAR-10.7B-Instruct-v1.0-GGUF --include "SOLAR-10.7B-Instruct-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:
huggingface-cli download tensorblock/SOLAR-10.7B-Instruct-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'