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

Commit

44038c1

verified ·

1 Parent(s): df933f7

Update README.md

Browse files

Files changed (1) hide show

README.md +26 -11

README.md CHANGED Viewed

@@ -20,25 +20,40 @@ DotWebs 1 has been trained on an extensive dataset comprising thousands of mimic
 # Benchmarks Showcase
-We evaluated DotWebs 1 using two benchmarks: Exam Sheet Evaluation (ESE), our in-house assessment designed around GoDotWebs workflows, and CDEval, a general-purpose evaluation standard. Together, these tests help us verify that the model performs reliably across the vast majority of real-world situations. The results are summarized below.
 ---
-# 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  |
 ---

 # 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
+| Model    | Score |
+| -------- | ------- |
+| 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.
 ---