Instructions to use marnelram/prn-modelv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marnelram/prn-modelv1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="marnelram/prn-modelv1",
	filename="model-q4_k_m.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 marnelram/prn-modelv1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marnelram/prn-modelv1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf marnelram/prn-modelv1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marnelram/prn-modelv1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf marnelram/prn-modelv1: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 marnelram/prn-modelv1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf marnelram/prn-modelv1: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 marnelram/prn-modelv1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf marnelram/prn-modelv1:Q4_K_M

Use Docker

docker model run hf.co/marnelram/prn-modelv1:Q4_K_M

LM Studio
Jan

vLLM

How to use marnelram/prn-modelv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marnelram/prn-modelv1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marnelram/prn-modelv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marnelram/prn-modelv1:Q4_K_M

Ollama
How to use marnelram/prn-modelv1 with Ollama:
```
ollama run hf.co/marnelram/prn-modelv1:Q4_K_M
```

Unsloth Studio new

How to use marnelram/prn-modelv1 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 marnelram/prn-modelv1 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 marnelram/prn-modelv1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for marnelram/prn-modelv1 to start chatting

Pi new

How to use marnelram/prn-modelv1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf marnelram/prn-modelv1: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": "marnelram/prn-modelv1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use marnelram/prn-modelv1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf marnelram/prn-modelv1:Q4_K_M

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 marnelram/prn-modelv1:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use marnelram/prn-modelv1 with Docker Model Runner:
```
docker model run hf.co/marnelram/prn-modelv1:Q4_K_M
```

Lemonade

How to use marnelram/prn-modelv1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull marnelram/prn-modelv1:Q4_K_M

Run and chat with the model

lemonade run user.prn-modelv1-Q4_K_M

List all available models

lemonade list

P℞N Model v1

A small fine-tuned LLM used in P℞N (ProRenata), a narrative bartending game set in a Victorian apothecary-style bar where drinks are named after medications. The model powers in-game NPC conversations with structured JSON output so the game can drive character expressions and tip calculation in real time.

Loaded in Godot via the NobodyWho GDExtension.

Files

File	Quantization	Size
`model-q4_k_m.gguf`	Q4_K_M	~2.5 GB

Output format

The model is constrained (GBNF grammar) to emit a single JSON object per turn:

{"reaction": "happy", "line": "Thanks, I needed to hear that."}

Reaction set

Reaction	Meaning
`happy`	Player was kind, helpful, or funny
`neutral`	Normal exchange, nothing notable
`sad`	Player said something hurtful or insensitive
`annoyed`	Player was rude, pushy, or weird
`exit`	Player crossed a line — conversation ends

The reaction updates the character portrait expression in real time; line is the spoken dialogue. Aggregated reactions feed the post-conversation tip multiplier.

Design note: reaction, not mood

Each character's baseline mood (heartbroken, stressed, celebrating, etc.) lives in the system prompt. The model's reaction field describes the response to the player's last message, not the character's overall emotional state. A heartbroken character can still react happy to a genuinely funny line.

Intended use

Driving NPC dialogue in P℞N (or similar narrative games)
Research / reference for structured-output character dialogue fine-tunes

Not intended as a general-purpose chat or instruction model.

Usage with NobodyWho (Godot)

@onready var model: NobodyWhoModel = $NobodyWhoModel
@onready var chat: NobodyWhoChat = $NobodyWhoChat

func _ready():
    model.model_path = "res://models/prn-modelv1/model-q4_k_m.gguf"
    chat.model_node = model
    chat.system_prompt = "You are Mei, a stressed pre-med student..."
    chat.start_worker()
    chat.response_finished.connect(func(r): print(r))
    chat.ask("Hey, rough day?")

See the NobodyWho docs for full API.

Limitations

Fine-tuned for one specific game's character set and tone — generalization outside that style is not guaranteed.
Output is constrained to the 5-reaction JSON schema; the model is not useful for freeform chat.
English only.

License

Released under Apache 2.0. The base model and any upstream licenses still apply.

Downloads last month: 29

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit