OpenReasoning-Nemotron-7B-GGUF

Quantized OpenReasoning-Nemotron-7B Models

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.

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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.

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Contact

For any inquiries or support, please contact us at [email protected] or visit our Website.