Instructions to use darwinagent/WIA-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darwinagent/WIA-4B with Transformers:

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

pipe = pipeline("text-generation", model="darwinagent/WIA-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darwinagent/WIA-4B")
model = AutoModelForCausalLM.from_pretrained("darwinagent/WIA-4B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use darwinagent/WIA-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darwinagent/WIA-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": "darwinagent/WIA-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darwinagent/WIA-4B

SGLang

How to use darwinagent/WIA-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 "darwinagent/WIA-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": "darwinagent/WIA-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 "darwinagent/WIA-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": "darwinagent/WIA-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

WIA-4B

WIA-4B is a 4B-parameter text world model from the World Intelligence Agent (WIA) project. It predicts the next observation produced by an action in heterogeneous digital environments, including Android, MCP, desktop OS, Search, SWE, Terminal, and Web. It is intended for world-model evaluation, synthetic agent training, and test-time action lookahead; it is not a general-purpose chat assistant.

The model is initialized from a Qwen3.5 4B backbone and trained with the WIA pipeline: continual pre-training (WIA-CPT), observation-only supervised fine-tuning (WIA-SFT), and GSPO-based world-model RL (WIA-WM).

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.

Stage ablation

OOD-only ablation under the DeepSeek-V4-Pro judge:

Stage	Android	MCP	OS	Search	SWE	Terminal	Web	Avg
Qwen3.5-4B-Base	35.88	33.29	47.07	26.43	33.14	36.00	40.21	35.74
WIA-CPT-4B	39.07	35.27	59.75	28.57	34.84	37.52	42.69	39.24
WIA-SFT-4B	37.82	39.14	44.81	34.40	49.57	51.58	46.88	43.84
WIA-WM-4B	27.19	44.10	48.40	36.84	60.06	55.87	49.27	46.34

The main gains are concentrated in trajectory-oriented domains: relative to the base model, the final model improves OOD SWE by 26.92 points and OOD Terminal by 19.87 points. The report also notes a small 4B RL regression on some UI-structured domains, especially Android.

Download

Install the Hugging Face tools and download the complete sharded checkpoint:

pip install -U "transformers>=5.8.0" huggingface_hub torch
hf download darwinagent/WIA-4B --local-dir ./WIA-4B

Usage

WIA-4B predicts an observation after an action. Keep the WIA prompt format and append ###[Observation] before generation.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "darwinagent/WIA-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.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. The report evaluates with long contexts; practical memory use depends on dtype, context length, batching, and attention implementation.

Citation

Please cite the WIA technical report when using this model. The report covers WIA-Corpus, WIA-Bench, WIA-WM, and WIA-WAM.

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