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 / model-files /serving.json

Harry19081

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 16 days ago

Raw History Blame Contribute Delete

1.2 kB

{
 "engine": "native-v2-vision",
 "checkpoint": "056A0-c31",
 "variant": "vision: 056A0-c31 language model (426 tensors, byte-identical) + Qwen/Qwen3.5-4B@851bf6e8 chat vision tower (297) + chat MTP head (15) = 738 keys; Qwen3_5ForConditionalGeneration",
 "model_sha256": "5a8553b9452e8a655774ef449bad20f79d02783ed258b65068a230db0e07da4c",
 "effort": "none",
 "prompt_format": "repeat_state_plain",
 "max_model_len": 131072,
 "thought_budget": 512,
 "noul_decisive_floor": 0.81,
 "temperature": "temperature.json",
 "temperature_sha256": "a0f72cd2d0a653e81051e5a0c77fc1a69131552a8102b580a93a6dbe7908b2da",
 "vision": {
  "server": "wald-serve-native-vision",
  "image_prompt_format": "plain",
  "max_images": 16,
  "vllm_args": [
   "--limit-mm-per-prompt",
   "{\"image\": 16, \"video\": 0}",
   "--mm-processor-kwargs",
   "{\"size\": {\"shortest_edge\": 65536, \"longest_edge\": 1048576}}",
   "--trust-request-chat-template"
  ]
 },
 "vllm": {
  "version": "0.30.0",
  "transformers": "5.17.0",
  "args": [
   "--max-model-len",
   "131072",
   "--gpu-memory-utilization",
   "0.85",
   "--max-num-seqs",
   "128",
   "--seed",
   "0"
  ],
  "env": {
   "VLLM_USE_FLASHINFER_SAMPLER": "0"
  }
 }
}

	{
	"engine": "native-v2-vision",
	"checkpoint": "056A0-c31",
	"variant": "vision: 056A0-c31 language model (426 tensors, byte-identical) + Qwen/Qwen3.5-4B@851bf6e8 chat vision tower (297) + chat MTP head (15) = 738 keys; Qwen3_5ForConditionalGeneration",
	"model_sha256": "5a8553b9452e8a655774ef449bad20f79d02783ed258b65068a230db0e07da4c",
	"effort": "none",
	"prompt_format": "repeat_state_plain",
	"max_model_len": 131072,
	"thought_budget": 512,
	"noul_decisive_floor": 0.81,
	"temperature": "temperature.json",
	"temperature_sha256": "a0f72cd2d0a653e81051e5a0c77fc1a69131552a8102b580a93a6dbe7908b2da",
	"vision": {
	"server": "wald-serve-native-vision",
	"image_prompt_format": "plain",
	"max_images": 16,
	"vllm_args": [
	"--limit-mm-per-prompt",
	"{\"image\": 16, \"video\": 0}",
	"--mm-processor-kwargs",
	"{\"size\": {\"shortest_edge\": 65536, \"longest_edge\": 1048576}}",
	"--trust-request-chat-template"
	]
	},
	"vllm": {
	"version": "0.30.0",
	"transformers": "5.17.0",
	"args": [
	"--max-model-len",
	"131072",
	"--gpu-memory-utilization",
	"0.85",
	"--max-num-seqs",
	"128",
	"--seed",
	"0"
	],
	"env": {
	"VLLM_USE_FLASHINFER_SAMPLER": "0"
	}
	}
	}