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 / RUNBOOK.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 5 days ago

preview code

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4.98 kB

Runbook

This revision (v1.2) holds Wald-Q4B v1.2, checkpoint 02600-f19. Section 1 serves it and reproduces its JevBench public read. Section 2 is the unchanged v1.1 runbook for the complete Decision Index run: that run belongs to v1.1 (checkpoint 022D0-f7), so download --revision v1.1 for it.

The server code, prompt format, tokenizer and temperature table are byte-identical in v1.1 and v1.2. The two weight shards differ, and serving.json declares effort none in v1.2 (high in v1.1). MANIFEST.json lists the exact model, tokenizer, temperature and code hashes of this revision.

1. Wald-Q4B v1.2

hf download org2ai/Wald-4B --revision v1.2 --local-dir ./Wald-Q4B-v1.2
cd Wald-Q4B-v1.2
./run.sh "$PWD"          # effort none (declared in serving.json), repeat_state_plain, context 131072

GET /health must report "effort": "none". v1.2's JevBench and JevAdvBench results were measured with effort none and repeat_state_plain on one NVIDIA RTX 5090 32 GB (vLLM 0.30.0, BF16); the JevAdvBench read used a 32,768-token context limit.

JevBench public set (204/231), with fstandhartinger/jevbench at 9ec6f15a:

cd jevbench          # a checkout of fstandhartinger/jevbench at 9ec6f15a
for T in easy original hard; do
  python -m jevbench.cli run --tasks datasets/public/$T.jsonl --adapter typesafe --endpoint http://127.0.0.1:8000 \
    --model jev-latest --key-env '' --reserve-usd 0 --cost-basis self_hosted_loopback_no_tariff \
    --results out/$T/results.jsonl --raw-dir out/raw-$T --ledger out/$T/ledger.jsonl --manifest out/$T/manifest.json \
    --run-label wald-q4b-v1.2-pub-$T
done
cat out/easy/results.jsonl out/original/results.jsonl out/hard/results.jsonl > out/results-all.jsonl
python -m jevbench.cli summarize --tasks datasets/public/easy.jsonl,datasets/public/original.jsonl,datasets/public/hard.jsonl \
  --results out/results-all.jsonl --public-export out/summary-all.json

JevAdvBench: send the benchmark's requests (JevAdvBench/JevAdvBench at 3218e05, one question per request) to the same endpoint and score the answers with the benchmark's own analysis code. The benchmark data is CC BY-NC 4.0 and is not redistributed here.

2. Wald-Q4B v1.1: complete Decision Index run

Reproduce Wald-Q4B 22D0-f7

Release: v1.1 · checkpoint 022D0-f7. Previous release: v1.0.

Download the immutable HF tag 22D0-f7 (or the full HF commit in the submission) into a fresh directory. Do not mix old v1.0 single-file weights with the new shards. MANIFEST.json lists the exact model, tokenizer, temperature and code hashes.

Convenient packaged server

hf download org2ai/Wald-4B --revision v1.1 --local-dir ./Wald-Q4B-22D
cd Wald-Q4B-22D
./run.sh "$PWD"

Default policy is high, repeat_state_plain, context 131072. Lower effort modes are alternative configurations with no 54.59 claim. GPU requirements: vLLM 0.30.0, BF16, NVIDIA RTX PRO 6000 96GB for comparison. The packaged server has parity tests against the reference below for prompts and answer probabilities. Scheduling may differ; latency is not established by those tests.

Frozen reference protocol

The full run used eval.systemone_vllm from the private development checkout; that checkout was not committed at launch. The included reference/ files freeze the release-time implementation, with hashes in MANIFEST.json. This code provenance limitation is disclosed rather than presenting a later public commit as the original launch commit.

python -m vllm.entrypoints.openai.api_server --model "$PWD" --served-model-name Wald-Q4B-022D0-f7-full021 --host 127.0.0.1 --port 8321 --max-model-len 131072 --gpu-memory-utilization 0.60 --max-num-seqs 128 --seed 0
# Separate terminal, same environment:
PYTHONPATH="$PWD/reference" python -m eval.systemone_vllm --vllm http://127.0.0.1:8321 --served Wald-Q4B-022D0-f7-full021 --gate 1.01 --budget 512 --temperature "$PWD/temperature.json" --wide knockout --template paren --max-model-len 131072 --return-raw --prompt-format repeat_state_plain --port 8421 --model-name Wald-Q4B-022D0-f7-full021

Install the pinned kit apolinario/decision-index@87d4650b42b377c0291a89c1f1a879f9b31082bf, rebuild and verify its complete suite locally, then run its http engine against port 8421. Suite payloads are not redistributed. No request or option pruning/truncation. Complete saved responses are untouched and suitable for maintainer rescoring.

Capacity: 131,072 tokens. Wide questions use ordered knockout over all supplied options. A thought that cannot fit falls back to the one-pass read. The full run had 0 unsupported and 0 errors. Concurrency ranged 16–40 request runners for throughput; timing is not the maintainer's serial admission gate. The 32-request preflight median 821.3 ms does not replace 750 private serial requests after warm-up.

Training/data/calibration limitations are in CONTAMINATION.md and PROVENANCE.md. Official admission remains pending.