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XythicK/Mag-Mell-R1-Uncensored-21B-GGUF
Mag-Mell-R1-Uncensored-21B-GGUF is a text generation model from XythicK. Use it when you need the model to write or continue text. It is set up for transformers.
Mag-Mell-R1-Uncensored-21B-GGUF is a quantized version of Mag-Mell-R1-Uncensored-21B, optimized for efficient inference with reduced memory usage and faster runtime while preserving as much of the original model quali…
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.gguf155 GB · 100%
From the Hugging Face model README
Mag-Mell-R1-Uncensored-21B-GGUF is a quantized version of Mag-Mell-R1-Uncensored-21B, optimized for efficient inference with reduced memory usage and faster runtime while preserving as much of the original model quality as possible.
This repository provides multiple quantized variants suitable for:
| Quant Format | File Name | Approx. Size | VRAM / RAM Needed | Notes |
|---|---|---|---|---|
| Q2_K | mag-mell-r1-uncensored-21b-q2_k.gguf | ~7.8 GB | ~8 GB | Extreme compression; noticeable quality loss |
| Q3_K_S | mag-mell-r1-uncensored-21b-q3_k_s.gguf | ~9 GB | ~10 GB | Smaller, faster, lower quality |
| Q3_K_M | mag-mell-r1-uncensored-21b-q3_k_m.gguf | ~10 GB | ~11 GB | Better balance than Q3_K_S |
| Q3_K_L | mag-mell-r1-uncensored-21b-q3_k_l.gguf | ~10.8 GB | ~11.5 GB | Highest-quality 3-bit variant |
| Q4_0 | mag-mell-r1-uncensored-21b-q4_0.gguf | ~11.7 GB | ~12.9 GB | Legacy format; simpler quantization |
| Q4_K_S | mag-mell-r1-uncensored-21b-q4_k_s.gguf | ~11.7 GB | ~13 GB | Smaller grouped 4-bit |
| Q4_K_M | mag-mell-r1-uncensored-21b-q4_k_m.gguf | ~12.4 GB | ~14 GB | Recommended default |
| Q5_0 | mag-mell-r1-uncensored-21b-q5_0.gguf | ~14.1 GB | ~16 GB | Higher quality, larger size |
| Q5_K_S | mag-mell-r1-uncensored-21b-q5_k_s.gguf | ~14 GB | ~15.1 GB | Efficient high-quality variant |
| Q5_K_M | mag-mell-r1-uncensored-21b-q5_K_M.gguf | ~14.5 GB | ~16 GB | Near-FP16 quality |
| Q6_K | mag-mell-r1-uncensored-21b-q6_k.gguf | ~16.8 GB | ~18 GB | Minimal quantization loss |
| Q8_0 | mag-mell-r1-uncensored-21b-q8_0.gguf | ~21.6 GB | ~23 GB | Maximum quality; large memory |
💡 Recommendation: Start with Q4_K_M for the best quality-to-performance ratio.
./main -m mag-mell-r1-uncensored-21b-q5_0.gguf -p "The World is beautiful isn't it?" -n 256
from llama_cpp import Llama
llm = Llama(
model_path="<MODEL_FILE>.gguf",
n_ctx=4096,
n_threads=8
)
print(llm("Your prompt here"))
Maintainer: M Mashhudur Rahim [XythicK]
Role:
Independent Machine Learning Researcher & Model Infrastructure Maintainer
(Focused on model quantization, optimization, and efficient deployment)
For issues, improvement requests, or additional quantization formats, please use the Hugging Face Discussions or Issues tab.
Thanks to the original model authors for their ongoing contributions to open AI research, and to Hugging Face and the open-source machine learning community for providing the tools and platforms that make efficient model sharing and deployment possible.