This repository provides quantized GGUF versions of the OpenReasoning-Nemotron-7B model. These 4-bit and 5-bit quantized variants retain the original model’s strengths in multimodal medical reasoning, while reducing memory and compute requirements—ideal for efficient inference on resource-constrained devices.

Model Overview

Original Model: OpenReasoning-Nemotron-7B
Quantized Versions:
- Q4_K_M (4-bit quantization)
- Q5_K_M (5-bit quantization)
Architecture: Decoder-only transformer
Base Model: Qwen2.5-7B-Instruct
Modalities: Text only
Developer: Qwen
License: GOVERNING TERMS: Use of the original models and above listed models are governed by the Creative Commons Attribution 4.0 International License (CC-BY-4.0). ADDITIONAL INFORMATION: Apache 2.0 License
Language: English

Quantization Details

Q4_K_M Version

Approx. ~70% size reduction
Lower memory footprint (~4.36 GB)
Best suited for deployment on edge devices or low-resource GPUs
Slight performance degradation in complex reasoning scenarios

Q5_K_M Version

Approx. ~66% size reduction
Higher fidelity (~5.07 GB)
Better performance retention, recommended when quality is a priority

Key Features

Expert-level reasoning capabilities across math, code, and scientific domains
Text-only instruction-following model optimized for multi-turn scientific question answering
Derived from Qwen2.5-7B-Instruct, further post-trained by NVIDIA on OpenReasoning datasets
Supports long-context inference with generation lengths of up to 64K tokens

Usage

This model is intended for developers and researchers who work on competitive math, code and science problems. It has been trained via only supervised fine-tuning to achieve strong scores on benchmarks.

llama.cpp (text-only)

./llama-cli -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF -p "What are the laplace transform"

Model Data

Dataset Overview

The original Qwen2.5-7B-Instruct model is built on top of the Qwen architecture and Post-trained on OpenReasoning datasets by NVIDIA:

LLM Component: Trained on diverse OpenReasoning datasets related to the above domains, including Science reports, Reasoning datasets, and Mathamatics and Coding datasets.

Recommended Use Cases

These quantized models are optimized for efficient inference while Maintaining Coding and mathamathics capabilities. Suggested use cases include:

Scientific question answering
Scientific Research and mathamatics concepts, coding lessions , etc.
Chatbot and assistant prototypes
Build interactive reasoning chat systems with coding capabilities.
Research & fine-tuning
Serve as a lightweight base for further task-specific tuning in coding.
Low-resource deployment
Run reasoning models on CPUs, edge devices, and lightweight GPUs.

Acknowledgments

These quantized models are based on the original work by Qwen and the NVIDIA development team.

Special thanks to:

The Nvidia team for developing and releasing the OpenReasoning-Nemotron-7B model.
Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our Website.