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": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("org2ai/Wald-4B")
model = AutoModelForMultimodalLM.from_pretrained("org2ai/Wald-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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?"
			}
		]
	}'

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

Wald-4B / PROVENANCE.md

Harry19081

Card: describe teacher models as large frontier models

91cf668 verified 29 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 large frontier models from our own templates (vendors and model names are not listed). A script inserted them, so the original question text is unchanged. Typos were produced by a script. A separate large-frontier-model call checked every row, and rows on which v1.1 changed its answer were checked again by a second large frontier model.
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`)