Instructions to use GoDotWebs/dotwebs-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GoDotWebs/dotwebs-1 with Transformers:

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

pipe = pipeline("text-generation", model="GoDotWebs/dotwebs-1")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("GoDotWebs/dotwebs-1")
model = AutoModelForMultimodalLM.from_pretrained("GoDotWebs/dotwebs-1", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GoDotWebs/dotwebs-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GoDotWebs/dotwebs-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GoDotWebs/dotwebs-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GoDotWebs/dotwebs-1

SGLang

How to use GoDotWebs/dotwebs-1 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 "GoDotWebs/dotwebs-1" \
    --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": "GoDotWebs/dotwebs-1",
		"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 "GoDotWebs/dotwebs-1" \
        --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": "GoDotWebs/dotwebs-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GoDotWebs/dotwebs-1 with Docker Model Runner:
```
docker model run hf.co/GoDotWebs/dotwebs-1
```

GoDotWebs commited on Jul 9

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@@ -26,7 +26,19 @@ We evaluated DotWebs 1 using two benchmarks: Exam Sheet Evaluation (ESE), our in
 # Enhanced Privacy Protection
-DotWebs 1 gives you the option to run on our own model instead of relying on third-party providers. Your data stays within our platform, protected by layered security controls designed to keep applications and personal information safe.
 ---

 # Enhanced Privacy Protection
+We evaluated DotWebs 1 using two benchmarks: Application Evaluation (AE), our in-house assessment designed around GoDotWebs workflows based on successful Y-combinator applications.
+In each run, the applicant model receives the company profile and drafts an answer. The judge model then scores that output against the original application that earned the company a spot in Y Combinator. Scoring breaks down into two criteria: Format Score and Closeness Score. Format Score measures how closely the answer follows the GoDotWebs workflow; Closeness Score measures how closely it matches the original model answer.
+For the judge model, we chose a cost-balanced option.
+| Judge Model |
+| -------- |
+| openai/gpt-oss-20b  |
+| Formula |
+| -------- |
+| composite = formatScore * 0.7 + closenessScore * 0.3  |
 ---