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

Libraries

How to use markdived/asg-v1 with Transformers:

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

pipe = pipeline("text-generation", model="markdived/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("markdived/asg-v1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use markdived/asg-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "markdived/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": "markdived/asg-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

asg-v1 / README.md

markdived

Duplicate from wasmdashai/asg-v1

b7fa3e3 9 days ago

preview code

Raw

History Blame Contribute Delete

1.33 kB

	---
	pipeline_tag: text-generation
	library_name: transformers
	---

	# 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 -> Encoder -> Scenario Planner -> Duration Planner -> Text Generator`

	## Usage

	```python
	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"])
	```

	The model is intended for authorized defensive training, tabletop exercises,
	detection engineering, control validation, and incident-response preparation.