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 8

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@@ -10,6 +10,8 @@ tags:
 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.

 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.