Instructions to use EssentialAI/rnj-1-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EssentialAI/rnj-1-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EssentialAI/rnj-1-instruct-GGUF",
	filename="Rnj-1-Instruct-8B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use EssentialAI/rnj-1-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/EssentialAI/rnj-1-instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use EssentialAI/rnj-1-instruct-GGUF with Ollama:
```
ollama run hf.co/EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EssentialAI/rnj-1-instruct-GGUF to start chatting

Pi new

How to use EssentialAI/rnj-1-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M

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": "rnj-1-instruct-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use EssentialAI/rnj-1-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
```

Lemonade

How to use EssentialAI/rnj-1-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EssentialAI/rnj-1-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.rnj-1-instruct-GGUF-Q4_K_M

List all available models

lemonade list

This is a GGUF-formatted checkpoint of rnj-1-instruct suitable for use in llama.cpp, Ollama, or others. This has been quantized with the Q4_K_M scheme, which results in model weights of size 4.8GB.

For llama.cpp, install (after version 7328, e.g., on Mac OSX brew install llama.cpp) and run either of these commands:

llama-cli -hf EssentialAI/rnj-1-instruct-GGUF
llama-server -hf EssentialAI/rnj-1-instruct-GGUF -c 0 # and open browser to localhost:8080

For Ollama, install (after version v0.13.3 -- versions can be found here) and run:

ollama run rnj-1

Downloads last month: 159

GGUF

Model size

8B params

Architecture

rnj1

Hardware compatibility

4-bit

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Collection including EssentialAI/rnj-1-instruct-GGUF

rnj-1

Collection

5 items • Updated Dec 19, 2025 • 39