Instructions to use darwinagent/WIA-122B-A10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darwinagent/WIA-122B-A10B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="darwinagent/WIA-122B-A10B")
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("darwinagent/WIA-122B-A10B")
model = AutoModelForMultimodalLM.from_pretrained("darwinagent/WIA-122B-A10B", 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 darwinagent/WIA-122B-A10B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darwinagent/WIA-122B-A10B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darwinagent/WIA-122B-A10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darwinagent/WIA-122B-A10B

SGLang

How to use darwinagent/WIA-122B-A10B 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 "darwinagent/WIA-122B-A10B" \
    --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": "darwinagent/WIA-122B-A10B",
		"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 "darwinagent/WIA-122B-A10B" \
        --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": "darwinagent/WIA-122B-A10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darwinagent/WIA-122B-A10B with Docker Model Runner:
```
docker model run hf.co/darwinagent/WIA-122B-A10B
```

WIA-122B-A10B

WIA-122B-A10B is a 122B-total-parameter, 10B-active-parameter MoE world-model variant from the World Intelligence Agent (WIA) project. It predicts action-conditioned observations across Android, MCP, desktop OS, Search, SWE, Terminal, and Web. It is intended for world-model evaluation and research; it is not a general-purpose chat assistant.

Unlike the other WIA-WM scales, this release is an RL-only variant initialized directly from Qwen3.5-122B-A10B. It does not use WIA-CPT or WIA world-model SFT. Its RL run uses a further-filtered/rebalanced RL-v1 set, think-mode rollouts, and a reward combining the LLM-judge score (0.9) with semantic similarity (0.1).

Evaluation

Results below are from the WIA technical report. Scores are on a 0--100 scale, evaluated by DeepSeek-V4-Pro; Avg is sample-weighted and MAvg is the macro average over seven domains.

WIA-Bench OOD

The complete OOD comparison from the technical report is reproduced below. Scores use the DeepSeek-V4-Pro judge; Avg is sample-weighted and MAvg is the macro average over seven domains.

Group	Model	Android	MCP	OS	Search	SWE	Terminal	Web	Avg	MAvg
Proprietary	GPT-5.6-luna	48.37	44.51	54.96	40.01	55.88	62.42	53.87	51.91	51.43
Proprietary	GPT-5.6-terra	48.34	43.55	56.14	40.46	57.68	61.79	54.57	52.38	51.79
Proprietary	GPT-5.6-sol	49.39	45.75	59.70	39.99	58.61	63.63	55.00	53.58	53.15
Proprietary	Claude-Sonnet-5	47.76	49.85	63.22	35.26	44.58	46.65	54.24	48.03	48.79
Proprietary	Claude-Opus-4.8	48.98	53.97	63.02	39.10	58.15	57.93	55.26	53.40	53.77
Open-source	GLM-5.3	47.20	50.23	55.58	31.10	32.68	53.88	51.03	44.98	45.96
Open-source	MiMo-v2.5-pro	45.80	46.18	59.78	38.20	49.73	53.43	51.50	49.09	49.23
Open-source	Qwen3.8-27B	41.08	39.40	53.34	34.88	48.47	51.84	47.51	45.46	45.22
Open-source	Qwen3.8-max	47.60	45.32	57.97	36.82	57.62	63.42	55.48	52.40	52.03
Open-source	DeepSeek-V4-Flash	46.74	44.46	58.93	38.40	50.51	55.53	50.77	49.40	49.33
Open-source	DeepSeek-V4-Pro	47.87	43.92	61.93	38.77	51.14	57.77	52.63	50.73	50.57
Qwen3.5	Qwen3.5-4B	39.92	38.38	55.29	30.44	37.26	39.50	45.24	40.46	40.86
Qwen3.5	Qwen3.5-9B	43.00	41.37	58.19	30.99	44.05	43.81	47.42	43.76	44.12
Qwen3.5	Qwen3.5-27B	47.84	42.56	64.29	33.75	48.10	55.09	52.78	49.15	49.20
Qwen3.5	Qwen3.5-35B-A3B	47.09	42.69	56.59	32.03	45.70	49.80	49.55	46.01	46.21
Qwen3.5	Qwen3.5-122B-A10B*	43.92	32.43	41.86	34.93	48.65	55.70	44.15	44.07	43.09
Qwen3.5	Qwen-AgentWorld	47.43	42.63	64.21	45.02	56.31	57.22	53.68	52.87	52.36
WIA-WM	WIA-WM-4B	27.19	44.10	48.40	36.84	60.06	55.87	49.27	46.34	45.96
WIA-WM	WIA-WM-9B	46.82	52.12	62.70	46.06	60.77	59.38	53.43	54.49	54.47
WIA-WM	WIA-WM-27B	50.51	52.53	63.42	46.36	59.92	60.73	54.52	55.45	55.43
WIA-WM	WIA-WM-35B-A3B	48.76	43.72	63.86	51.33	59.19	62.52	54.95	55.72	54.90
WIA-WM	WIA-WM-122B-A10B*	50.59	40.00	64.71	58.18	60.46	64.62	53.59	57.35	56.02

WIA-Bench ID

The complete ID comparison from the technical report is reproduced below.

Group	Model	Android	MCP	OS	Search	SWE	Terminal	Web	Avg	MAvg
Proprietary	GPT-5.6-sol	43.62	61.02	33.08	50.66	53.11	59.96	62.38	52.04	51.98
Proprietary	GPT-5.6-terra	41.57	61.98	33.08	47.32	52.81	59.25	60.94	51.05	50.99
Proprietary	GPT-5.6-luna	41.29	60.03	32.43	45.40	51.29	55.28	58.33	49.11	49.15
Proprietary	Claude-Opus-4.8	41.59	59.08	33.00	63.50	56.46	65.91	66.21	55.76	55.11
Proprietary	Claude-Sonnet-5	40.08	60.28	32.27	58.46	38.72	60.40	63.96	50.75	50.59
Open-source	GLM-5.3	41.20	58.63	33.20	54.74	32.09	63.22	64.52	49.64	49.66
Open-source	MiMo-v2.5-pro	40.93	59.61	32.39	51.34	47.36	54.35	64.16	50.12	50.02
Open-source	Qwen3.8-27B	36.69	55.67	30.75	50.87	43.50	52.67	61.15	47.59	47.33
Open-source	Qwen3.8-max	41.18	58.32	32.39	61.00	51.06	65.09	65.82	54.06	53.55
Open-source	DeepSeek-V4-Flash	40.64	59.23	32.39	56.41	44.87	56.97	64.40	50.90	50.70
Open-source	DeepSeek-V4-Pro	41.54	59.06	32.31	55.72	45.65	55.51	64.51	50.76	50.61
Qwen3.5	Qwen3.5-4B	35.61	54.57	30.96	45.63	33.14	40.22	56.33	42.20	42.35
Qwen3.5	Qwen3.5-9B	36.80	55.09	31.81	38.29	37.16	38.97	60.23	42.39	42.62
Qwen3.5	Qwen3.5-27B	41.24	57.26	32.09	45.92	43.77	57.37	64.72	48.91	48.91
Qwen3.5	Qwen3.5-35B-A3B	37.50	56.75	32.21	39.15	41.76	50.03	61.36	45.47	45.54
Qwen3.5	Qwen-AgentWorld	39.03	56.99	32.49	63.68	45.60	61.33	64.54	52.51	51.95
WIA-WM	WIA-WM-4B	27.12	65.26	30.76	82.59	57.52	75.76	63.24	59.17	57.46
WIA-WM	WIA-WM-9B	41.61	63.89	32.79	84.31	60.87	73.96	68.69	61.99	60.87
WIA-WM	WIA-WM-27B	43.21	67.74	35.28	88.54	66.24	80.85	73.84	66.41	65.10
WIA-WM	WIA-WM-35B-A3B	43.49	67.45	34.39	89.30	64.18	76.36	71.86	65.04	63.86
WIA-WM	WIA-WM-122B-A10B*	41.44	64.33	33.01	86.66	57.74	71.43	66.21	61.16	60.12

*The 122B-A10B configuration differs from the other WIA-WM scales; it is RL-only and uses a different RL data and reward setup.

Ablation

Because this is an RL-only variant, the report provides an OOD comparison against its original backbone rather than the CPT→SFT→RL stage ablation used for the other four models.

Stage	Android	MCP	OS	Search	SWE	Terminal	Web	Avg
Qwen3.5-122B-A10B	43.92	32.43	41.86	34.93	48.65	55.70	44.15	44.07
WIA-WM-122B-A10B (RL-only)	50.59	40.00	64.71	58.18	60.46	64.62	53.59	57.35

The RL-only run improves every OOD domain over the backbone, with the largest gains on Search (+23.25) and OS (+22.85), and the smallest gains on Android (+6.67) and MCP (+7.57).

Download

pip install -U "transformers>=5.8.0" huggingface_hub torch
hf download darwinagent/WIA-122B-A10B --local-dir ./WIA-122B-A10B

Usage

WIA-122B-A10B predicts an observation after an action. Keep the WIA prompt format and append ###[Observation] before generation. This is a very large model and normally requires a multi-GPU setup or an inference service.

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

model_id = "darwinagent/WIA-122B-A10B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "system", "content": "Operate as a language world model and report only the resulting observation."},
    {"role": "user", "content": "Task: inspect the repository.\nCurrent observation: the shell is open.\nAction: shell_exec(command='pwd', cwd='/workspace', timeout_sec=30)\n###[Observation]"},
]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
with torch.inference_mode():
    output = model.generate(inputs, max_new_tokens=16384, do_sample=False)
print(tokenizer.decode(output[0, inputs.shape[-1]:], skip_special_tokens=True))

Use a GPU configuration appropriate for the model and context length. Practical memory use depends on dtype, context length, batching, and attention implementation.

Citation

Please cite the WIA technical report when using this model.

Downloads last month: 843

Safetensors

Model size

126B params

Tensor type

BF16

Collection including darwinagent/WIA-122B-A10B

WIA Models

Collection

WIA world-model checkpoints. • 5 items • Updated 10 days ago • 1