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)

# pip install -U transformers accelerate
# 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Laya

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

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

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 / llms.txt

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 7 days ago

Raw History Blame Contribute Delete

3.32 kB

	# Wald-Q4B v1.2

	> Open-weight 4B decision model. Given a state and typed questions (choice, yes/no, score), it returns a calibrated probability for every option through a Jev-compatible `POST /v1/systemone` API. Built on Qwen3.5-4B-Base; Apache-2.0 weights and serving code; self-hosted on one NVIDIA GPU with vLLM. Independent project, not affiliated with or endorsed by TypeSafe AI.

	Key facts:
	- Hugging Face repository: org2ai/Wald-4B (earlier name Wald-4B; moved from Harry19081/Wald-4B on 2026-10-01, old URLs redirect). Releases: `v1.2` (tag `v1.2`, robustness release, checkpoint 02600-f19; also the weights on `main` since 2026-10-01) and `v1.1` (tag `v1.1`, general release, checkpoint 022D0-f7). Pin a revision when downloading. GitHub: org2AI/wald-4b.
	- Uses: tool selection, agent routing, classification, deciding whether to ask the user a clarifying question.
	- Effort levels: `none` (one pass, no generated tokens), `low`, `medium`, `high`, `high-k2`…`high-k8`. Default effort: `high` for v1.1, `none` for v1.2. v1.2 is a one-pass model; thinking is evaluated on v1.1.
	- v1.2 robustness (JevAdvBench, 812 questions, nine attack types, effort `none`, self-run): mean flip rate 4.6 % (v1.1 9.2 %, Jev 1.13 6.1 %). Cost: clean accuracy on the 143 human-reviewed questions 76.2 % (v1.1 79.0 %).
	- JevBench public set (231 items), effort `none`: v1.2 204/231, ECE 0.045; v1.1 203/231, ECE 0.041, p50 33 ms / p95 168 ms on one RTX PRO 6000. Self-scored with JevBench's harness; the public items were a development scoreboard, not held out. Leaderboard row for v1.1 requested in fstandhartinger/jevbench issue #146; v1.2 is not submitted.
	- Decision Index 0.2.1 complete suite, v1.1 with effort `high`: 54.59 balanced-skill index (v1.2 has no complete-suite run). Author-run; submission apolinario/decision-index PR #30 awaits maintainer validation.

	## Docs

	- [Model card](https://huggingface.co/org2ai/Wald-4B): what it is, benchmarks, comparisons with Jev, Kev and Laya, FAQ, limits
	- [API reference](https://huggingface.co/org2ai/Wald-4B/blob/main/docs/api.md): request and response JSON with examples
	- [RUNBOOK.md](https://huggingface.co/org2ai/Wald-4B/blob/main/RUNBOOK.md): serving and exact evaluation settings
	- [Chinese model card](https://huggingface.co/org2ai/Wald-4B/blob/main/docs/readmes/README.zh.md)

	## Evidence

	- [Decision Index results dataset](https://huggingface.co/datasets/org2ai/Wald-Q4B-decision-index-results): untouched responses and scores
	- [Decision Index submission PR #30](https://github.com/apolinario/decision-index/pull/30)
	- [JevBench request issue #146](https://github.com/fstandhartinger/jevbench/issues/146)
	- [v1.2 evaluation summary](https://huggingface.co/org2ai/Wald-4B/blob/v1.2/evaluation/v1.2/summary.json): robustness and non-regression numbers for v1.2
	- [model-info.json](https://huggingface.co/org2ai/Wald-4B/blob/main/model-info.json): machine-readable facts

	## Optional

	- [PROVENANCE.md](https://huggingface.co/org2ai/Wald-4B/blob/main/PROVENANCE.md): training-data sources and their terms
	- [CONTAMINATION.md](https://huggingface.co/org2ai/Wald-4B/blob/main/CONTAMINATION.md): evaluation caveats
	- [CITATION.cff](https://huggingface.co/org2ai/Wald-4B/blob/main/CITATION.cff)
	- [Server source](https://github.com/org2AI/wald-4b/tree/main/server)