Instructions to use darwinagent/WIA-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darwinagent/WIA-9B with Transformers:

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

pipe = pipeline("text-generation", model="darwinagent/WIA-9B")
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-9B")
model = AutoModelForCausalLM.from_pretrained("darwinagent/WIA-9B", 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-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darwinagent/WIA-9B"
# 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-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darwinagent/WIA-9B

SGLang

How to use darwinagent/WIA-9B 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-9B" \
    --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-9B",
		"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-9B" \
        --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-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darwinagent/WIA-9B with Docker Model Runner:
```
docker model run hf.co/darwinagent/WIA-9B
```

LightningCreeper commited on 11 days ago

Commit

ec44e6f

verified ·

1 Parent(s): c68214c

Add complete WIA-Bench comparison tables

Browse files

Files changed (1) hide show

README.md +53 -14

README.md CHANGED Viewed

@@ -22,23 +22,62 @@ Results below are from the WIA technical report. Scores are on a 0--100 scale, e
 ### WIA-Bench OOD
-| Model | Android | MCP | OS | Search | SWE | Terminal | Web | Avg | MAvg |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| WIA-WM-4B | 27.19 | 44.10 | 48.40 | 36.84 | 60.06 | 55.87 | 49.27 | 46.34 | 45.96 |
-| WIA-WM-9B | 46.82 | 52.12 | 62.70 | 46.06 | 60.77 | 59.38 | 53.43 | 54.49 | 54.47 |
-| WIA-WM-27B | 50.51 | 52.53 | 63.42 | 46.36 | 59.92 | 60.73 | 54.52 | 55.45 | 55.43 |
-| WIA-WM-35B-A3B | 48.76 | 43.72 | 63.86 | 51.33 | 59.19 | 62.52 | 54.95 | 55.72 | 54.90 |
-| 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
-| Model | Android | MCP | OS | Search | SWE | Terminal | Web | Avg | MAvg |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| WIA-WM-4B | 27.12 | 65.26 | 30.76 | 82.59 | 57.52 | 75.76 | 63.24 | 59.17 | 57.46 |
-| WIA-WM-9B | 41.61 | 63.89 | 32.79 | 84.31 | 60.87 | 73.96 | 68.69 | 61.99 | 60.87 |
-| WIA-WM-27B | 43.21 | 67.74 | 35.28 | 88.54 | 66.24 | 80.85 | 73.84 | 66.41 | 65.10 |
-| WIA-WM-35B-A3B | 43.49 | 67.45 | 34.39 | 89.30 | 64.18 | 76.36 | 71.86 | 65.04 | 63.86 |
-| WIA-WM-122B-A10B | 41.44 | 64.33 | 33.01 | 86.66 | 57.74 | 71.43 | 66.21 | 61.16 | 60.12 |
 ### Stage ablation

 ### 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