Instructions to use org2ai/Wald-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use org2ai/Wald-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="org2ai/Wald-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("org2ai/Wald-4B")
model = AutoModelForCausalLM.from_pretrained("org2ai/Wald-4B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

How to use org2ai/Wald-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "org2ai/Wald-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "org2ai/Wald-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "org2ai/Wald-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "org2ai/Wald-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

How to use org2ai/Wald-4B 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: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:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Wald-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use org2ai/Wald-4B 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: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: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 / PROVENANCE.md

Harry19081

main = Wald-Q4B v1.2 (02600-f19, robustness release): weights from v1.2-release, v1.2 serving.json (effort none) and runbook; card: v1.2 on main, v1.1 at tag v1.1

981b91b verified 2 days ago

preview code

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Training data

WaldGen is the name of our internally generated decision-training corpus. It covers structured decisions and short reasoning. The model also uses public training datasets; WaldGen does not rename or claim authorship of those sources.

Public-source acknowledgments include ACOS, NLI4CT, RAGTruth, and VAST. RAGTruth contains third-party MS MARCO/Yelp contexts. MS MARCO terms limit dataset use to non-commercial research; other source-text permissions are not uniformly specified. Model/code licensing does not grant rights in those texts or constitute commercial clearance.

Base model: Qwen3.5-4B-Base, Apache-2.0. No training rows or benchmark request payloads are distributed here.

v1.2 (checkpoint `02600-f19`)

v1.2 adds one LoRA stage on v1.1. The v1.1 notes above apply unchanged.

Questions. 5,300 questions drawn from v1.1's own training text. No new source dataset was added.
Perturbed rows. 8,064 perturbed copies of those questions (13,674 training rows with repeats and 5,000 unperturbed replay rows). Each perturbed row has a short distracting or pressuring text inserted into the question, after an option or into the state, or is a paraphrase or a typo variant.
Text written by a model. The inserted texts and the paraphrases were written by Claude Haiku (Anthropic) from our own templates. A script inserted them, so the original question text is unchanged. Typos were produced by a script. A separate Claude Haiku call checked every row, and rows on which v1.1 changed its answer were checked again by Claude Sonnet.
Targets. v1.1's own answer distribution on the unperturbed question.
Benchmarks. No JevAdvBench text was used. JevAdvBench is evaluation-only.

No training rows or benchmark request payloads are distributed here.

Training data

v1.2 (checkpoint 02600-f19)

v1.2 (checkpoint `02600-f19`)