Instructions to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF",
	filename="model-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-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 ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

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 ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

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 ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Use Docker

docker model run hf.co/ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

LM Studio
Jan
Ollama
How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with Ollama:
```
ollama run hf.co/ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16
```

Unsloth Studio

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-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 ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-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 ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF to start chatting

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16
```

Lemonade

How to use ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF:F16

Run and chat with the model

lemonade run user.OpenCodeReasoning-Nemotron-14B-GGUF-F16

List all available models

lemonade list

nvidia/OpenCodeReasoning-Nemotron-14B - GGUF

This repository contains GGUF quantizations of nvidia/OpenCodeReasoning-Nemotron-14B.

About GGUF

GGUF is a quantization method that allows you to run large language models on consumer hardware by reducing the precision of the model weights.

Files

Filename	Quant type	File Size	Description
model-f16.gguf	f16	Large	Original precision
model-q4_0.gguf	Q4_0	Small	4-bit quantization
model-q4_1.gguf	Q4_1	Small	4-bit quantization (higher quality)
model-q5_0.gguf	Q5_0	Medium	5-bit quantization
model-q5_1.gguf	Q5_1	Medium	5-bit quantization (higher quality)
model-q8_0.gguf	Q8_0	Large	8-bit quantization

Usage

You can use these models with llama.cpp or any other GGUF-compatible inference engine.

llama.cpp

./llama-cli -m model-q4_0.gguf -p "Your prompt here"

Python (using llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="model-q4_0.gguf")
output = llm("Your prompt here", max_tokens=512)
print(output['choices'][0]['text'])

Original Model

This is a quantized version of nvidia/OpenCodeReasoning-Nemotron-14B. Please refer to the original model card for more information about the model's capabilities, training data, and usage guidelines.

Conversion Details

Converted using llama.cpp
Original model downloaded from Hugging Face
Multiple quantization levels provided for different use cases

License

This model inherits the license from the original model. Please check the original model's license for usage terms.

Downloads last month: 8

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Finetuned

nvidia/OpenCodeReasoning-Nemotron-14B

Quantized

(7)

this model

Collection including ReallyFloppyPenguin/OpenCodeReasoning-Nemotron-14B-GGUF

GGUFs

Collection

20 items • Updated Mar 2 • 1