Instructions to use EREN121232/MAJESTIC-FIN-R1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EREN121232/MAJESTIC-FIN-R1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EREN121232/MAJESTIC-FIN-R1-gguf",
	filename="MAJESTIC-FIN-R1-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use EREN121232/MAJESTIC-FIN-R1-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf EREN121232/MAJESTIC-FIN-R1-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 EREN121232/MAJESTIC-FIN-R1-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf EREN121232/MAJESTIC-FIN-R1-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 EREN121232/MAJESTIC-FIN-R1-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16

Use Docker

docker model run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16

LM Studio
Jan

vLLM

How to use EREN121232/MAJESTIC-FIN-R1-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EREN121232/MAJESTIC-FIN-R1-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": "EREN121232/MAJESTIC-FIN-R1-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16

Ollama
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Ollama:
```
ollama run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16
```

Unsloth Studio new

How to use EREN121232/MAJESTIC-FIN-R1-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 EREN121232/MAJESTIC-FIN-R1-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 EREN121232/MAJESTIC-FIN-R1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EREN121232/MAJESTIC-FIN-R1-gguf to start chatting

Pi new

How to use EREN121232/MAJESTIC-FIN-R1-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EREN121232/MAJESTIC-FIN-R1-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": "EREN121232/MAJESTIC-FIN-R1-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EREN121232/MAJESTIC-FIN-R1-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EREN121232/MAJESTIC-FIN-R1-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 EREN121232/MAJESTIC-FIN-R1-gguf:F16

Run Hermes

hermes

Docker Model Runner
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Docker Model Runner:
```
docker model run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16
```

Lemonade

How to use EREN121232/MAJESTIC-FIN-R1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EREN121232/MAJESTIC-FIN-R1-gguf:F16

Run and chat with the model

lemonade run user.MAJESTIC-FIN-R1-gguf-F16

List all available models

lemonade list

MAJESTIC-FIN-R1 GGUF

MAJESTIC-FIN-R1 is a fine-tuned LiquidAI/LFM2-2.6B model exported to GGUF for Ollama, llama.cpp, and lightweight CPU deployment.

Available files

MAJESTIC-FIN-R1-F16.gguf: highest-fidelity GGUF export.
MAJESTIC-FIN-R1-Q8_0.gguf: smaller GGUF export for Ollama and free CPU hosting.
template: Ollama chat template for this model family.
params: default Ollama runtime parameters.
Modelfile: local Ollama import file.

Run with Ollama from Hugging Face

ollama run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:Q8_0

Run with Ollama locally

Download MAJESTIC-FIN-R1-Q8_0.gguf and Modelfile.
Keep them in the same folder.
Run:

ollama create majestic-fin-r1 -f Modelfile
ollama run majestic-fin-r1

Free hosted demo and API

A public Hugging Face Space can serve the Q8_0 build on free CPU hardware. The companion Space for this repo is:

https://huggingface.co/spaces/EREN121232/MAJESTIC-FIN-R1-Free-API

Once the Space is live, use the footer link Use via API to inspect endpoints, or call the /chat endpoint directly from Python, JavaScript, or curl.

Downloads last month: 391

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for EREN121232/MAJESTIC-FIN-R1-gguf

Base model

LiquidAI/LFM2-2.6B

Quantized

(21)

this model