metadata
base_model:
- theprint/DevilsAdvocate-7B
library_name: gguf
pipeline_tag: text-generation
language: en
license: mit
tags:
- gguf
- quantized
- llama.cpp
- devilsadvocate-7b
model_type: llama
quantized_by: theprint
datasets:
- theprint/Advocate-9.4k
DevilsAdvocate-7B - GGUF Quantized
Quantized GGUF versions of DevilsAdvocate-7B for use with llama.cpp and other GGUF-compatible inference engines.
Original Model
- Base model: Qwen/Qwen2.5-7B-Instruct
- Fine-tuned model: theprint/DevilsAdvocate-7B
- Quantized by: theprint
Available Quantizations
DevilsAdvocate-7B-f16.gguf(14531.9 MB) - 16-bit float (original precision, largest file)DevilsAdvocate-7B-q3_k_m.gguf(3632.0 MB) - 3-bit quantization (medium quality)DevilsAdvocate-7B-q4_k_m.gguf(4466.1 MB) - 4-bit quantization (medium, recommended for most use cases)DevilsAdvocate-7B-q5_k_m.gguf(5192.6 MB) - 5-bit quantization (medium, good quality)DevilsAdvocate-7B-q6_k.gguf(5964.5 MB) - 6-bit quantization (high quality)DevilsAdvocate-7B-q8_0.gguf(7723.4 MB) - 8-bit quantization (very high quality)
Usage
With llama.cpp
# Download recommended quantization
wget https://huggingface.co/theprint/DevilsAdvocate-7B-GGUF/resolve/main/DevilsAdvocate-7B-q4_k_m.gguf
# Run inference
./llama.cpp/main -m DevilsAdvocate-7B-q4_k_m.gguf \
-p "Your prompt here" \
-n 256 \
--temp 0.7 \
--top-p 0.9
With other GGUF tools
These files are compatible with:
- llama.cpp
- Ollama (import as custom model)
- KoboldCpp
- text-generation-webui
Quantization Info
Recommended: q4_k_m provides the best balance of size, speed, and quality for most use cases.
For maximum quality: Use q8_0 or f16
For maximum speed/smallest size: Use q3_k_m or q4_k_s
License
mit
Citation
@misc{devilsadvocate_7b_gguf,
title={DevilsAdvocate-7B GGUF Quantized Models},
author={theprint},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/theprint/DevilsAdvocate-7B-GGUF}
}