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 / server

Commit History

wald-serve 0.2.1: Noul decisive floor 0.81 (serving.json noul_decisive_floor; yes/no P inside (0.19, 0.81) moves to the nearer edge after calibration; choice/score untouched)

4e381a8
verified

Harry19081 commited on about 13 hours ago

Wald-Q4B v2 (04701-c22 + Qwen3.5-4B vision tower): main = v2; v1.x at tags v1.2 / v1.2-main / v1.1 / v1.0

ef7481d

org2ai commited on 2 days ago

Card: GGUF / llama.cpp / Ollama section (EN + ZH), tags; wald-serve 0.1.1 (llama.cpp backend, vLLM path unchanged); MANIFEST with the GGUF files

9662694
verified

Harry19081 commited on 9 days ago

Wald-4B v1.0

78ebf43

Harry19081 commited on 13 days ago

Commit History

wald-serve 0.2.1: Noul decisive floor 0.81 (serving.json noul_decisive_floor; yes/no P inside (0.19, 0.81) moves to the nearer edge after calibration; choice/score untouched) 4e381a8 verified

Wald-Q4B v2 (04701-c22 + Qwen3.5-4B vision tower): main = v2; v1.x at tags v1.2 / v1.2-main / v1.1 / v1.0 ef7481d

Card: GGUF / llama.cpp / Ollama section (EN + ZH), tags; wald-serve 0.1.1 (llama.cpp backend, vLLM path unchanged); MANIFEST with the GGUF files 9662694 verified

Wald-4B v1.0 78ebf43

wald-serve 0.2.1: Noul decisive floor 0.81 (serving.json noul_decisive_floor; yes/no P inside (0.19, 0.81) moves to the nearer edge after calibration; choice/score untouched)

4e381a8
verified

Wald-Q4B v2 (04701-c22 + Qwen3.5-4B vision tower): main = v2; v1.x at tags v1.2 / v1.2-main / v1.1 / v1.0

ef7481d

Card: GGUF / llama.cpp / Ollama section (EN + ZH), tags; wald-serve 0.1.1 (llama.cpp backend, vLLM path unchanged); MANIFEST with the GGUF files

9662694
verified

Wald-4B v1.0

78ebf43