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")
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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DotWebs 1 (dotwebs-1)

Our frontier-class model. Trained on mimicked GoDotWebs practical use cases and equipped with a built-in reliability architecture that actively minimizes hallucinations for more accurate, trustworthy results.

The Next Tier Solution

DotWebs 1 has been trained on an extensive dataset comprising thousands of mimicked real-world applications, mirroring the expertise gained by a human who has authored and optimized thousands of such documents. The purpose of the model is to make the application process significantly faster and smoother, while delivering feedback that is far more realistic, precise, and personalized, moving beyond generic responses.

Benchmarks Showcase

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

Models	Scores
GoDotWebs/DotWebs-1	66.00%
Google/Gemma-3n-E4B-it	58.48%
Nvidia/Nemotron-3-Ultra-550B-a55b	57.47%
Qwen/Qwen3.5-9B	52.10%
Llama-3.3-70B-Instruct-Turbo	51.38%

Model Specifications

Model Name: GoDotWebs/DotWebs-1

Base Model: Qwen/Qwen3.5-9B

Primary Usage: Form auto-filling, answers evaluation, feedback simulations.

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.

Our Next Step

Our roadmap focuses on scaling DotWebs 1 or upcoming models to meet growing demand while advancing faster inference, higher accuracy, and more realistic outputs tailored to GoDotWebs applications.

License

DotWebs 1 is made available through GoDotWebs and is intended for use within our platform. GoDotWebs retains rights to our branding, custom model work, and related materials. Your use is governed by your plan and our Terms of Service. For licensing questions, contact info@godotwebs.com.

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Safetensors

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BF16

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Model tree for GoDotWebs/dotwebs-1

Quantizations

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