prithivMLmods/Research-Reasoning-Qwen-F32-GGUF

Nemotron-Research-Reasoning-Qwen-1.5B-GGUF

Nemotron-Research-Reasoning-Qwen-1.5B is the world’s leading 1.5B open-weight model for complex reasoning tasks such as mathematical problems, coding challenges, scientific questions, and logic puzzles. It is trained using the ProRL algorithm on a diverse and comprehensive set of datasets. Our model has achieved impressive results, outperforming Deepseek’s 1.5B model by a large margin on a broad range of tasks, including math, coding, and GPQA.

Model Files

File Name	Format	Size	Precision	Use Case
Nemotron-Research-Reasoning-Qwen-1.5B.F32.gguf	GGUF	7.11 GB	F32	Highest precision, research use
Nemotron-Research-Reasoning-Qwen-1.5B.BF16.gguf	GGUF	3.56 GB	BF16	High precision, balanced performance
Nemotron-Research-Reasoning-Qwen-1.5B.F16.gguf	GGUF	3.56 GB	F16	High precision, memory efficient
Nemotron-Research-Reasoning-Qwen-1.5B.Q8_0.gguf	GGUF	1.89 GB	Q8_0	Good quality, moderate compression
Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_M.gguf	GGUF	1.29 GB	Q5_K_M	Balanced quality/size (recommended)
Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_S.gguf	GGUF	1.26 GB	Q5_K_S	Good quality, smaller size
Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_M.gguf	GGUF	1.12 GB	Q4_K_M	Good balance for most users
Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_S.gguf	GGUF	1.07 GB	Q4_K_S	Decent quality, compact size
Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_L.gguf	GGUF	980 MB	Q3_K_L	Lower quality, very compact
Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_M.gguf	GGUF	924 MB	Q3_K_M	Fast inference, limited quality
Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_S.gguf	GGUF	861 MB	Q3_K_S	Fastest inference, basic quality
Nemotron-Research-Reasoning-Qwen-1.5B.Q2_K.gguf	GGUF	753 MB	Q2_K	Minimal size, experimental use

Quick Selection Guide

• For Research/Development: Use F32 or BF16 for maximum accuracy
• For Production (Recommended): Use Q5_K_M for best quality/performance balance
• For Resource-Constrained Environments: Use Q4_K_M or Q4_K_S
• For Edge Devices: Use Q3_K_M or Q2_K for minimal footprint

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):