Instructions to use DakkaWolf/Trouper-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DakkaWolf/Trouper-12B-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 DakkaWolf/Trouper-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DakkaWolf/Trouper-12B-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 DakkaWolf/Trouper-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DakkaWolf/Trouper-12B-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 DakkaWolf/Trouper-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DakkaWolf/Trouper-12B-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 DakkaWolf/Trouper-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DakkaWolf/Trouper-12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DakkaWolf/Trouper-12B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DakkaWolf/Trouper-12B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DakkaWolf/Trouper-12B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DakkaWolf/Trouper-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DakkaWolf/Trouper-12B-GGUF:Q4_K_M

Ollama
How to use DakkaWolf/Trouper-12B-GGUF with Ollama:
```
ollama run hf.co/DakkaWolf/Trouper-12B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use DakkaWolf/Trouper-12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DakkaWolf/Trouper-12B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DakkaWolf/Trouper-12B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DakkaWolf/Trouper-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/DakkaWolf/Trouper-12B-GGUF:Q4_K_M
```

Lemonade

How to use DakkaWolf/Trouper-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DakkaWolf/Trouper-12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Trouper-12B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DakkaWolf/Trouper-12B-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 DakkaWolf/Trouper-12B-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 DakkaWolf/Trouper-12B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DakkaWolf/Trouper-12B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DakkaWolf/Trouper-12B-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 "DakkaWolf/Trouper-12B-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"

Trouper-12B GGUF by DarwinAnim8or

From the original model card:

A character roleplay model trained on the custom "Actors" dataset, fine-tuned from Mistral-Nemo-Base-12B. This model was made to expand on the things I learned from TinyRP, and to overcome certain limitations I found from it; also on an entirely new dataset made just for this model.

This model writes more naturally, less like "AI"; even more so than the 24B model I'm also releasing. I suppose this is because the 12B model saw less synthethic data, and is thus less likely to use phrases typical in AI writing & prose.

Using an importance matrix actually increases KLD (lowers quality) for Q8_0 and Q6_K, so I didn't use one for those quants. For anything smaller the importance matrix provides better quality with no inference performance penalty.

The importance matrix was computed using the Bartowski's calibration_datav3.txt dataset, which has been proven to produce good results for all kinds of tasks.

The KLD metric was measured against the wikitext-2 validation split with 4096 tokens of context.

Quant	BPW	Size (GiB)	KLD99	iMatrix
BF16	16.00	22.81	N/A	N/A
Q8_0	8.50	12.12	0.005840
Q6_K	6.56	9.36	0.023252
Q5_K_M	5.70	8.12	0.044965	X
Q5_K_S	5.56	7.93	0.053320	X
Q4_K_M	4.88	6.96	0.130179	X
Q4_K_S	4.65	6.62	0.156863	X
Q3_K_L	4.28	6.10	0.391981	X
Q3_K_M	3.97	5.66	0.453535	X
Q3_K_S	3.61	5.15	1.682329	X
Q2_K	3.12	4.45	2.297512	X
Q2_K_S	2.93	4.18	2.207151	X
IQ4_NL	4.63	6.60	0.165374	X
IQ4_XS	4.40	6.27	0.171350	X
IQ3_M	3.73	5.32	0.513056	X
IQ3_S	3.63	5.17	0.544215	X
IQ3_XS	3.46	4.93	0.733339	X
IQ3_XXS	3.23	4.60	1.081350	X
IQ2_M	2.89	4.12	1.818141	X
IQ2_S	2.70	3.85	2.526351	X
IQ2_XS	2.55	3.64	2.840284	X
IQ2_XXS	2.34	3.34	3.772680	X
IQ1_M	2.10	2.99	5.374491	X
IQ1_S	1.95	2.79	6.467200	X