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AaryanK/
NousCoder-14B-GGUF

Name: NousCoder-14B-GGUF
Availability: InStock

gguf

quantized

text-generation

NousResearch/NousCoder-14B

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NousCoder-14B-GGUF

GGUF format model files for NousCoder-14B.

Available Files

Filename	Quant Type	Size	Description
`NousCoder-14B.q2_k.gguf`	Q2_K	5.36 GB	Smallest size, lowest quality. Best for testing if model works.
`NousCoder-14B.q3_k_l.gguf`	Q3_K_L (Large)	7.36 GB	Better quality 3-bit quantization.
`NousCoder-14B.q3_k_m.gguf`	Q3_K_M (Medium)	6.82 GB	Small size with slightly better quality than Q3_K_S.
`NousCoder-14B.q3_k_s.gguf`	Q3_K_S (Small)	6.20 GB	Very small, low quality. Not recommended for most uses.
`NousCoder-14B.q4_0.gguf`	Q4_0	7.93 GB	Basic 4-bit quantization. Good balance of size and quality.
`NousCoder-14B.q4_1.gguf`	Q4_1	8.74 GB	4-bit with higher accuracy than Q4_0.
`NousCoder-14B.q4_k_m.gguf`	Q4_K_M (Medium)	8.38 GB	K-quant 4-bit, best balance of size and quality. Most popular choice.
`NousCoder-14B.q4_k_s.gguf`	Q4_K_S (Small)	7.98 GB	K-quant 4-bit, optimized for smaller size.
`NousCoder-14B.q5_0.gguf`	Q5_0	9.56 GB	Basic 5-bit quantization. Higher quality than Q4.
`NousCoder-14B.q5_1.gguf`	Q5_1	10.37 GB	5-bit with higher accuracy than Q5_0.
`NousCoder-14B.q5_k_m.gguf`	Q5_K_M (Medium)	9.79 GB	K-quant 5-bit, excellent quality-to-size ratio.
`NousCoder-14B.q5_k_s.gguf`	Q5_K_S (Small)	9.56 GB	K-quant 5-bit, good quality with reasonable size.
`NousCoder-14B.q6_k.gguf`	Q6_K	11.29 GB	6-bit quantization. Very high quality, larger size.
`NousCoder-14B.q8_0.gguf`	Q8_0	14.62 GB	8-bit quantization. Near-original quality, largest quantized size.

Usage

bash

# With llama.cpp
./llama-cli -m NousCoder-14B.q4_k_m.gguf -p "Your prompt" -n 128

Original Model Card

NousCoder-14B

We introduce NousCoder-14B, a competitive programming model post-trained on Qwen3-14B via reinforcement learning. On LiveCodeBench v6 (08/01/2024 - 05/01/2025), we achieve a Pass@1 accuracy of 67.87%, up 7.08% from the baseline Pass@1 accuracy of 60.79% of Qwen3-14B. We trained on 24k verifiable coding problems using 48 B200s over the course of four days.

Acknowledgements

I would like to thank my mentor, Roger Jin, Dakota Mahan, Teknium, and others at the Nous Research team for their invaluable support throughout this project. I would also like to thank Together AI and Agentica for their immensely helpful blog posts on DeepCoder-14B. Finally, thank you to Modal and Lambda for their generous support by providing me with credits.

AaryanK/NousCoder-14B-GGUF