Instructions to use angecoded/VillageMind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use angecoded/VillageMind 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 angecoded/VillageMind:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf angecoded/VillageMind:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/angecoded/VillageMind:Q4_K_M

LM Studio
Jan

vLLM

How to use angecoded/VillageMind with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/angecoded/VillageMind:Q4_K_M

Ollama
How to use angecoded/VillageMind with Ollama:
```
ollama run hf.co/angecoded/VillageMind:Q4_K_M
```

Unsloth Studio

How to use angecoded/VillageMind 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 angecoded/VillageMind 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 angecoded/VillageMind to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for angecoded/VillageMind to start chatting

How to use angecoded/VillageMind with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf angecoded/VillageMind: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": "angecoded/VillageMind:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use angecoded/VillageMind with Docker Model Runner:
```
docker model run hf.co/angecoded/VillageMind:Q4_K_M
```

Lemonade

How to use angecoded/VillageMind with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull angecoded/VillageMind:Q4_K_M

Run and chat with the model

lemonade run user.VillageMind-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use angecoded/VillageMind with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf angecoded/VillageMind: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 angecoded/VillageMind:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use angecoded/VillageMind with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf angecoded/VillageMind:Q4_K_M

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 "angecoded/VillageMind:Q4_K_M" \
  --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"

VillageMind 0.1-dev

VillageMind is a compact local language model fine-tuned for natural NPC dialogue in the Living Villages Minecraft mod project.

The goal is not to let an LLM control the Minecraft world directly. VillageMind is responsible for language, personality and context-aware dialogue, while the Living Villages game logic remains authoritative for inventory, relationships, reputation, jobs, combat, ownership, world state and other gameplay facts.

Status: Development release candidate
Current release: VillageMind-0.1-dev-Q4_K_M.gguf
Base model: Qwen/Qwen3-1.7B
Format: GGUF
Quantization: Q4_K_M
Primary validated language: German

Purpose

VillageMind is designed for NPCs that should feel like inhabitants of a Minecraft world rather than generic assistants.

The fine-tuning focuses on short, natural responses to situations such as:

strangers asking for favors
friends requesting help
hostile players
attacks and threats
private property and theft
work and free time
following or refusing to follow a player
apologies and relationships
rumors and uncertainty
village emergencies such as fire
grief and social reactions
donations
inventory questions
village resource shortages

The intended integration is Living Villages, where residents can have persistent personalities, memories, relationships, jobs, inventories and social behavior.

Architecture

VillageMind is derived from:

Qwen/Qwen3-1.7B

Training approach:

QLoRA / LoRA fine-tuning
4-bit NF4 training
approximately 32,000 training examples
1,200 held-out evaluation examples
best adapter selected by evaluation loss
final model merged with the Qwen3-1.7B base model
converted to GGUF
quantized to Q4_K_M

The selected adapter reached a best evaluation loss of approximately 0.1117 during training.

Evaluation

VillageMind was evaluated with a separate hand-designed final examination containing 180 gameplay-oriented dialogue cases.

The critical subset contains 50 tests covering behaviors that should not fail in normal gameplay, including inventory truthfulness, ownership boundaries, stranger/enemy behavior and uncertainty around rumors.

Best adapter

Metric	Result
Tests passed	176 / 180
Overall score	97.78%
Critical tests	50 / 50
Critical score	100%

Q4_K_M GGUF

The final GGUF was tested again after merge, conversion and quantization using the same 180-case suite.

Metric	Result
Tests passed	174 / 180
Overall score	96.67%
Critical tests	50 / 50
Critical score	100%
Release candidate	Yes

The tested GGUF has the SHA-256 checksum:

475b1b819c42a8667ea2d101f68020f0c8e10315bfbbbd5a21d3227436a268b1

The repository also contains the generated manifest and release-candidate evaluation metadata.

Known limitation

The main known weakness of VillageMind 0.1-dev is resource-shortage attribution.

In a small number of evaluation cases, the model correctly recognized that the village lacked a resource but referred to the wrong resource in its response.

For example, if the authoritative game state says that wheat is scarce, the model may occasionally talk about iron instead.

For this reason, Living Villages is designed around a strict separation:

Minecraft world state
        ↓
Living Villages game logic
(authoritative facts and actions)
        ↓
VillageMind
(language generation)
        ↓
Fact / action validation
        ↓
Player

VillageMind must therefore not be treated as the authoritative source for:

item inventories
resource counts
ownership
memories
reputation values
relationship state
jobs or task feasibility
combat state
world changes
spawning or removing items
any other persistent gameplay state

These facts should be supplied and validated by the host application.

Language

VillageMind 0.1-dev has been specifically evaluated in German.

The Qwen3 base model is multilingual, and future Living Villages versions are intended to support both German and English dialogue based on the player's Minecraft language setting. However, English behavior has not yet received the same dedicated VillageMind 0.1-dev evaluation, so this release should not be described as equally validated in English yet.

Example behavior

Stranger

Player:

Bring me some iron.

Resident:

Du bist neu hier. Ich nehme keine Aufträge von Fremden an.

Private property

Player:

I'm taking this from your chest.

Resident:

Finger weg. Das gehört dir nicht.

Rumor

Player:

Is the rumor true?

Resident:

Ich hab was gehört, aber ich weiß nicht, ob's stimmt.

Inventory

If the game provides the resident inventory as:

7 coal
2 bread

VillageMind can respond:

Ich hab gerade 7 Kohle, 2 Brot. Mehr nicht.

The host application should still validate factual responses against the authoritative game state.

Recommended use

The current release is intended primarily for:

Living Villages development
local Minecraft NPC dialogue
experimentation with state-aware game characters
local inference through llama.cpp-compatible runtimes

It is not intended as:

a general-purpose assistant
a source of factual real-world information
an autonomous authority over game state
a replacement for deterministic gameplay logic

GGUF file

Primary release file:

VillageMind-0.1-dev-Q4_K_M.gguf

Recommended runtime:

llama.cpp or a compatible GGUF inference runtime

The exact integration parameters for Living Villages may change during development.

Repository files

VillageMind-0.1-dev-Q4_K_M.gguf
VillageMind-0.1-dev-manifest.json
release-candidate-gguf.json
README.md

Base model and license

VillageMind is a fine-tuned derivative of Qwen3-1.7B by the Qwen Team.

Base model:

https://huggingface.co/Qwen/Qwen3-1.7B

Qwen3-1.7B is distributed under the Apache License 2.0. VillageMind is likewise published under Apache-2.0.

Please review the base model's documentation and license when redistributing or building on VillageMind.

Project status

VillageMind 0.1-dev is the first development model candidate intended for integration into Living Villages.

The project is under active development. Evaluation results describe this specific development test suite and should not be interpreted as a general benchmark score for language-model quality.

Deutsch

VillageMind ist ein lokal ausführbares Sprachmodell für natürliche NPC-Dialoge im Minecraft-Mod-Projekt Living Villages.

Das Modell soll nicht selbst über die Minecraft-Welt bestimmen. Living Villages bleibt die autoritative Instanz für Inventare, Beziehungen, Ruf, Eigentum, Aufgaben, Kampf, Ressourcen und Weltzustände. VillageMind übernimmt hauptsächlich die natürliche Formulierung der NPC-Reaktionen.

Aktueller Stand

Basis: Qwen/Qwen3-1.7B
Fine-Tuning: QLoRA / LoRA
GGUF: Q4_K_M
Best-Adapter-Abschlussprüfung: 176/180 (97,78%)
Kritische Tests Best Adapter: 50/50
GGUF-Abschlussprüfung: 174/180 (96,67%)
Kritische Tests GGUF: 50/50
Status: Release Candidate bestanden

Bekannte Schwäche

Bei einigen Tests zu knappen Dorfressourcen nennt VillageMind gelegentlich die falsche Ressource. Deshalb werden solche Aussagen in Living Villages nicht ungeprüft als Wahrheit übernommen.

Die Minecraft-Mod liefert die echten Fakten und kann VillageMinds Antwort anschließend validieren.

Sprache

Die Version 0.1-dev wurde gezielt auf Deutsch geprüft. Englisch soll im späteren Living-Villages-Einsatz ebenfalls unterstützt werden, wurde für diesen Modellstand aber noch nicht mit einer gleichwertigen Abschlussprüfung validiert.

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GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for angecoded/VillageMind

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1033)

this model