Instructions to use theprint/GameMaster-v1-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/GameMaster-v1-2B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/GameMaster-v1-2B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use theprint/GameMaster-v1-2B-GGUF 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 theprint/GameMaster-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/GameMaster-v1-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf theprint/GameMaster-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/GameMaster-v1-2B-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 theprint/GameMaster-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/GameMaster-v1-2B-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 theprint/GameMaster-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/GameMaster-v1-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/theprint/GameMaster-v1-2B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use theprint/GameMaster-v1-2B-GGUF with Ollama:
```
ollama run hf.co/theprint/GameMaster-v1-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use theprint/GameMaster-v1-2B-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 theprint/GameMaster-v1-2B-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 theprint/GameMaster-v1-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/GameMaster-v1-2B-GGUF to start chatting

How to use theprint/GameMaster-v1-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/GameMaster-v1-2B-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": "theprint/GameMaster-v1-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use theprint/GameMaster-v1-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/GameMaster-v1-2B-GGUF: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 theprint/GameMaster-v1-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use theprint/GameMaster-v1-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/GameMaster-v1-2B-GGUF: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 "theprint/GameMaster-v1-2B-GGUF: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"

Docker Model Runner
How to use theprint/GameMaster-v1-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/theprint/GameMaster-v1-2B-GGUF:Q4_K_M
```

Lemonade

How to use theprint/GameMaster-v1-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/GameMaster-v1-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GameMaster-v1-2B-GGUF-Q4_K_M

List all available models

lemonade list

GameMaster-v1-2B (GGUF)

A fine-tuned version of unsloth/Qwen3.5-2B trained on GameMastering sharegpt data using Auto-SFT — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the GameMastering sharegpt dataset. Expect improved performance on tasks similar to those represented in the training data.

Model Details

Property	Value
Base model	`unsloth/Qwen3.5-2B`
Training data	`data/GameMastering-sharegpt.json`
Fine-tuning epochs	2
Fine-tuning date	2026-07-19
Fine-tuning method	LoRA (merged to full 16-bit)

Training Hyperparameters

LoRA

Parameter	Value
`r`	`4`
`alpha`	`16`
`dropout`	`0.04`
`target_modules`	`['q_proj', 'v_proj', 'k_proj', 'o_proj']`

Training

Parameter	Value
`learning_rate`	`1e-05`
`batch_size`	`4`
`gradient_accumulation_steps`	`8`
`warmup_ratio`	`0.03`
`max_seq_length`	`2048`
`quantization`	`none`

GGUF Files

These quantized GGUF files can be used directly with llama.cpp, Ollama, LM Studio, and other compatible runtimes.

File	Description
`GameMaster-v1-2B-GGUF-BF16.gguf`	BF16
`GameMaster-v1-2B-GGUF-Q8_0.gguf`	8-bit — near-lossless, larger file
`GameMaster-v1-2B-GGUF-Q6_K.gguf`	6-bit — high quality
`GameMaster-v1-2B-GGUF-Q5_K_M.gguf`	5-bit medium — good quality/size balance
`GameMaster-v1-2B-GGUF-Q5_K_S.gguf`	Q5_K_S
`GameMaster-v1-2B-GGUF-Q4_K_M.gguf`	4-bit medium — recommended for most use cases
`GameMaster-v1-2B-GGUF-Q4_K_S.gguf`	Q4_K_S
`GameMaster-v1-2B-GGUF-Q3_K_L.gguf`	Q3_K_L
`GameMaster-v1-2B-GGUF-Q3_K_M.gguf`	Q3_K_M
`GameMaster-v1-2B-GGUF-Q3_K_S.gguf`	Q3_K_S
`GameMaster-v1-2B-GGUF-Q2_K.gguf`	2-bit — smallest size, lowest quality
`GameMaster-v1-2B-GGUF-IQ4_XS.gguf`	IQ4_XS
`GameMaster-v1-2B-GGUF-IQ4_NL.gguf`	IQ4_NL
`GameMaster-v1-2B-GGUF-TQ2_0.gguf`	TQ2_0

Generated by Auto-SFT

Downloads last month: 1,115

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for theprint/GameMaster-v1-2B-GGUF

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

unsloth/Qwen3.5-2B

Adapter

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Collection including theprint/GameMaster-v1-2B-GGUF

Qwen 3.5 2B Experts

Collection

Collection of Qwen 3.5 2B models that have been fine-tunes in various subdomains. This collection contains the quantized versions. • 8 items • Updated 5 days ago