Instructions to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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 prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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 prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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": "prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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 "prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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": "prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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 "prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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": "prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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 prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-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 prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenReasoning-Nemotron-1.5B-F32-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

OpenReasoning-Nemotron-1.5B-F32-GGUF

OpenReasoning-Nemotron-1.5B is a large language model (LLM) which is a derivative of Qwen2.5-1.5B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning about math, code and science solution generation. We evaluated this model with up to 64K output tokens. OpenReasoning-Nemotron models can be used in a "heavy" mode by starting multiple parallel generations and combining them together via generative solution selection (GenSelect). To add this "skill" we follow the original GenSelect training pipeline except we do not train on the selection summary but use the full reasoning trace of DeepSeek R1 0528 671B instead. We only train models to select the best solution for math problems but surprisingly find that this capability directly generalizes to code and science questions! With this "heavy" GenSelect inference mode, OpenReasoning-Nemotron-32B model surpasses O3 (High) on math and coding benchmarks.

Model File

Quant Type	File Size	Filename
F32	6.18 GB	OpenReasoning-Nemotron-1.5B.F32.gguf
F16	3.09 GB	OpenReasoning-Nemotron-1.5B.F16.gguf
BF16	3.09 GB	OpenReasoning-Nemotron-1.5B.BF16.gguf
Q8_0	1.65 GB	OpenReasoning-Nemotron-1.5B.Q8_0.gguf
Q6_K	1.27 GB	OpenReasoning-Nemotron-1.5B.Q6_K.gguf
Q5_K_M	1.13 GB	OpenReasoning-Nemotron-1.5B.Q5_K_M.gguf
Q5_K_S	1.1 GB	OpenReasoning-Nemotron-1.5B.Q5_K_S.gguf
Q4_K_M	986 MB	OpenReasoning-Nemotron-1.5B.Q4_K_M.gguf
Q4_K_S	940 MB	OpenReasoning-Nemotron-1.5B.Q4_K_S.gguf
Q3_K_L	880 MB	OpenReasoning-Nemotron-1.5B.Q3_K_L.gguf
Q3_K_M	824 MB	OpenReasoning-Nemotron-1.5B.Q3_K_M.gguf
Q3_K_S	761 MB	OpenReasoning-Nemotron-1.5B.Q3_K_S.gguf
Q2_K	676 MB	OpenReasoning-Nemotron-1.5B.Q2_K.gguf

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):

Downloads last month: 147

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/OpenReasoning-Nemotron-1.5B-F32-GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

nvidia/OpenReasoning-Nemotron-1.5B

Quantized

(18)

this model