metadata
license: other
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE
language:
- de
- en
tags:
- sft
- dpo
- TensorBlock
- GGUF
base_model: VAGOsolutions/SauerkrautLM-Qwen-32b
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
VAGOsolutions/SauerkrautLM-Qwen-32b - GGUF
This repo contains GGUF format model files for VAGOsolutions/SauerkrautLM-Qwen-32b.
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 |
|---|---|---|---|
| SauerkrautLM-Qwen-32b-Q2_K.gguf | Q2_K | 11.383 GB | smallest, significant quality loss - not recommended for most purposes |
| SauerkrautLM-Qwen-32b-Q3_K_S.gguf | Q3_K_S | 13.303 GB | very small, high quality loss |
| SauerkrautLM-Qwen-32b-Q3_K_M.gguf | Q3_K_M | 14.729 GB | very small, high quality loss |
| SauerkrautLM-Qwen-32b-Q3_K_L.gguf | Q3_K_L | 15.942 GB | small, substantial quality loss |
| SauerkrautLM-Qwen-32b-Q4_0.gguf | Q4_0 | 17.228 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| SauerkrautLM-Qwen-32b-Q4_K_S.gguf | Q4_K_S | 17.361 GB | small, greater quality loss |
| SauerkrautLM-Qwen-32b-Q4_K_M.gguf | Q4_K_M | 18.346 GB | medium, balanced quality - recommended |
| SauerkrautLM-Qwen-32b-Q5_0.gguf | Q5_0 | 20.922 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| SauerkrautLM-Qwen-32b-Q5_K_S.gguf | Q5_K_S | 20.922 GB | large, low quality loss - recommended |
| SauerkrautLM-Qwen-32b-Q5_K_M.gguf | Q5_K_M | 21.498 GB | large, very low quality loss - recommended |
| SauerkrautLM-Qwen-32b-Q6_K.gguf | Q6_K | 24.847 GB | very large, extremely low quality loss |
| SauerkrautLM-Qwen-32b-Q8_0.gguf | Q8_0 | 32.180 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/SauerkrautLM-Qwen-32b-GGUF --include "SauerkrautLM-Qwen-32b-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/SauerkrautLM-Qwen-32b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'