Alcoft/Qwen_Qwen3-14B-GGUF

Quant	Size	Description
Q2_K	5.36 GB	Not recommended for most people. Very low quality.
Q2_K_L	6.07 GB	Not recommended for most people. Uses Q8_0 for output and embedding, and Q2_K for everything else. Very low quality.
Q2_K_XL	7.42 GB	Not recommended for most people. Uses F16 for output and embedding, and Q2_K for everything else. Very low quality.
Q3_K_S	6.2 GB	Not recommended for most people. Prefer any bigger Q3_K quantization. Low quality.
Q3_K_M	6.82 GB	Not recommended for most people. Low quality.
Q3_K_L	7.36 GB	Not recommended for most people. Low quality.
Q3_K_XL	7.99 GB	Not recommended for most people. Uses Q8_0 for output and embedding, and Q3_K_L for everything else. Low quality.
Q3_K_XXL	9.35 GB	Not recommended for most people. Uses F16 for output and embedding, and Q3_K_L for everything else. Low quality.
Q4_K_S	7.98 GB	Recommended. Slightly low quality.
Q4_K_M	8.38 GB	Recommended. Decent quality for most use cases.
Q4_K_L	8.92 GB	Recommended. Uses Q8_0 for output and embedding, and Q4_K_M for everything else. Decent quality.
Q4_K_XL	10.28 GB	Recommended. Uses F16 for output and embedding, and Q4_K_M for everything else. Decent quality.
Q5_K_S	9.56 GB	Recommended. High quality.
Q5_K_M	9.79 GB	Recommended. High quality.
Q5_K_L	10.24 GB	Recommended. Uses Q8_0 for output and embedding, and Q5_K_M for everything else. High quality.
Q5_K_XL	11.6 GB	Recommended. Uses F16 for output and embedding, and Q5_K_M for everything else. High quality.
Q6_K	11.29 GB	Recommended. Very high quality.
Q6_K_L	11.64 GB	Recommended. Uses Q8_0 for output and embedding, and Q6_K for everything else. Very high quality.
Q6_K_XL	13.0 GB	Recommended. Uses F16 for output and embedding, and Q6_K for everything else. Very high quality.
Q8_0	14.62 GB	Recommended. Quality almost like F16.
Q8_K_XL	15.98 GB	Recommended. Uses F16 for output and embedding, and Q8_0 for everything else. Quality almost like F16.
F16	27.51 GB	Not recommended. Overkill. Prefer Q8_0.
ORIGINAL (BF16)	27.51 GB	Not recommended. Overkill. Prefer Q8_0.

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Quantized using TAO71-AI AutoQuantizer. You can check out the original model card here.