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

prn-modelv1 / README.md

marnelram

Add model card

5704037 verified 11 days ago

preview code

raw

history blame contribute delete

2.81 kB

	---
	license: apache-2.0
	library_name: gguf
	tags:
	- gguf
	- game-ai
	- npc-dialogue
	- structured-output
	- roleplay
	language:
	- en
	pipeline_tag: text-generation
	---

	# 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](https://godotengine.org/) via the [NobodyWho](https://github.com/nobodywho-ooo/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:

	```json
	{"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)

	```gdscript
	@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](https://docs.nobodywho.ooo) 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.