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URL: https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF

⇱ prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF · Hugging Face

GCIRS-Reasoning-1.5B-R1-GGUF

GCIRS-Reasoning-1.5B-R1 is a research-grade reasoning model fine-tuned from Qwen2.5-1.5B-Instruct, focused on non-fictional reasoning, factual consistency, and scientific depth. Trained with reinforcement learning using the Big Reasoning Traces dataset from DeepSeek, this model is tailored for complex analytical tasks and scientific rigor in high-stakes or research environments.

Model Files

File Name Format Size Precision Use Case
GCIRS-Reasoning-1.5B-R1.F32.gguf GGUF 7.11 GB F32 Highest precision, research use
GCIRS-Reasoning-1.5B-R1.BF16.gguf GGUF 3.56 GB BF16 High precision, balanced performance
GCIRS-Reasoning-1.5B-R1.F16.gguf GGUF 3.56 GB F16 High precision, memory efficient
GCIRS-Reasoning-1.5B-R1.Q8_0.gguf GGUF 1.89 GB Q8_0 Excellent quality, moderate compression
GCIRS-Reasoning-1.5B-R1.Q6_K.gguf GGUF 1.46 GB Q6_K Very good quality, good compression
GCIRS-Reasoning-1.5B-R1.Q5_K_M.gguf GGUF 1.29 GB Q5_K_M Balanced quality/size (recommended)
GCIRS-Reasoning-1.5B-R1.Q5_K_S.gguf GGUF 1.26 GB Q5_K_S Good quality, smaller size
GCIRS-Reasoning-1.5B-R1.Q4_K_M.gguf GGUF 1.12 GB Q4_K_M Good balance for most users
GCIRS-Reasoning-1.5B-R1.Q4_K_S.gguf GGUF 1.07 GB Q4_K_S Decent quality, compact size
GCIRS-Reasoning-1.5B-R1.Q3_K_L.gguf GGUF 980 MB Q3_K_L Lower quality, very compact
GCIRS-Reasoning-1.5B-R1.Q3_K_M.gguf GGUF 924 MB Q3_K_M Fast inference, limited quality
GCIRS-Reasoning-1.5B-R1.Q3_K_S.gguf GGUF 861 MB Q3_K_S Fastest inference, basic quality
GCIRS-Reasoning-1.5B-R1.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 or Q6_K for best quality/performance balance
  • For General Use: Use Q4_K_M or Q4_K_S for good performance
  • For Resource-Constrained Environments: Use Q3_K_M or Q3_K_L
  • For Edge Devices: Use 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):

👁 image.png

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