Instructions to use org2ai/Wald-4B-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 org2ai/Wald-4B-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 org2ai/Wald-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf org2ai/Wald-4B-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 org2ai/Wald-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf org2ai/Wald-4B-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 org2ai/Wald-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf org2ai/Wald-4B-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 org2ai/Wald-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf org2ai/Wald-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/org2ai/Wald-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use org2ai/Wald-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "org2ai/Wald-4B-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": "org2ai/Wald-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/org2ai/Wald-4B-GGUF:Q4_K_M

Ollama
How to use org2ai/Wald-4B-GGUF with Ollama:
```
ollama run hf.co/org2ai/Wald-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use org2ai/Wald-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf org2ai/Wald-4B-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": "org2ai/Wald-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use org2ai/Wald-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/org2ai/Wald-4B-GGUF:Q4_K_M
```

Lemonade

How to use org2ai/Wald-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull org2ai/Wald-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Wald-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use org2ai/Wald-4B-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 org2ai/Wald-4B-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 org2ai/Wald-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use org2ai/Wald-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf org2ai/Wald-4B-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 "org2ai/Wald-4B-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"

Wald-4B GGUF

GGUF files of Wald-Q4B v2.1 (tag v2.1) for llama.cpp: an open 4B decision model that returns a calibrated probability for every option.

File	Size	Same answer as BF16 (JevBench public, one pass)
`Wald-4B-v2.1-Q8_0.gguf`	4.5 GB	231/231
`Wald-4B-v2.1-Q6_K.gguf`	3.5 GB	226/231
`Wald-4B-v2.1-Q5_K_M.gguf`	3.1 GB	226/231
`Wald-4B-v2.1-Q4_K_M.gguf`	2.7 GB	225/231
`Wald-4B-v2-mmproj-F16.gguf` (vision projector for llama.cpp's multimodal tools; the vision tower is unchanged since v2, so this file serves v2.1 too)	0.67 GB

Quick start

hf download org2ai/Wald-4B-GGUF --include "Wald-4B-v2.1-Q4_K_M.gguf" "v2.1/*" --local-dir ./wald-gguf
pip install "./wald-gguf/v2.1/server" transformers
wald-serve-native --gguf ./wald-gguf/Wald-4B-v2.1-Q4_K_M.gguf --tokenizer-dir ./wald-gguf/v2.1 --effort none --port 8000

How it works

One pass, text only: the bundled server reads a probability for every option from the model's option-letter logits (Qwen chat template), calibrated with the same frozen temperature table as the BF16 release.
--effort auto (Auto 0.7) also runs here but has not been parity-checked on GGUF; for images, use the BF16 release.
On a 1,000-request Decision Index sample (5,922 questions), Q8_0 gives the BF16 answer on 99.5 % of questions and Q4_K_M on 97.3 %. All numbers: evaluation/v2.1-gguf-parity.json.