Instructions to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF", dtype="auto")

llama-cpp-python

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF",
	filename="openReasoning-nemotron-7B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

SGLang

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenReasoning-Nemotron-7B-GGUF-Q4_K_M

List all available models

lemonade list

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.

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.

Downloads last month: 6

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

Model tree for SandLogicTechnologies/OpenReasoning-Nemotron-7B-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

nvidia/OpenReasoning-Nemotron-7B

Quantized

(14)

this model