Instructions to use wasmdashai/asg-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wasmdashai/asg-v1 with Transformers:

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

pipe = pipeline("text-generation", model="wasmdashai/asg-v1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("wasmdashai/asg-v1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wasmdashai/asg-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/wasmdashai/asg-v1

SGLang

How to use wasmdashai/asg-v1 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 "wasmdashai/asg-v1" \
    --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": "wasmdashai/asg-v1",
		"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 "wasmdashai/asg-v1" \
        --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": "wasmdashai/asg-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wasmdashai/asg-v1 with Docker Model Runner:
```
docker model run hf.co/wasmdashai/asg-v1
```

ASGTransformer

ASGTransformer is a unified, catalog-grounded defensive cybersecurity scenario model. It bundles the semantic encoder, scenario planner, duration planner, professional text renderer, and knowledge catalog in one Hugging Face repository.

Pipeline

Input Text
     │
     ▼
 Semantic Encoder
     │
     ▼
 Scenario Planner
     │
     ▼
 Duration Planner
     │
     ▼
 Professional Text Generator
     │
     ▼
 Structured Defensive Scenario

Features

Unified end-to-end defensive scenario generation.
Knowledge catalog integration for realistic enterprise scenarios.
Multi-language support.
Professional scenario rendering.
Duration estimation.
Compatible with the Hugging Face Transformers ecosystem.
CPU and GPU inference support.
Designed for authorized defensive cybersecurity training.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "wasmdashai/asg-v1"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)

result = model.generate_scenario(
    tokenizer,
    (
        "Create an authorized defensive enterprise scenario focused on "
        "phishing awareness, credential protection, and response readiness."
    ),
    language="en",
    max_new_tokens=384,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

print(result["text"])
print(result["estimated_duration_minutes"])
print(result["scenario_type"])