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

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 11 days ago

preview code

Raw History Blame

2.08 kB

	# 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](https://github.com/NUSTM/ACOS), [NLI4CT](https://github.com/ai-systems/Task-2-SemEval-2024), [RAGTruth](https://github.com/ParticleMedia/RAGTruth), and [VAST](https://github.com/emilyallaway/zero-shot-stance). RAGTruth contains third-party MS MARCO/Yelp contexts. [MS MARCO terms](https://microsoft.github.io/msmarco/) 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](https://huggingface.co/Qwen/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.