Instructions to use markdived/asg-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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"architectures": [
"ASGTransformerForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_asg_transformer.ASGTransformerConfig",
"AutoModelForCausalLM": "modeling_asg_transformer.ASGTransformerForCausalLM"
},
"base_model_config": {
"_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct",
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"chunk_size_feed_forward": 0,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 896,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"initializer_range": 0.02,
"intermediate_size": 4864,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"output_attentions": false,
"output_hidden_states": false,
"pad_token_id": null,
"problem_type": null,
"return_dict": true,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.13.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
},
"bos_token_id": null,
"dtype": "bfloat16",
"duration_bins": [
15,
30,
45,
60,
90,
120,
180,
240
],
"duration_loss_weight": 0.1,
"eos_token_id": 151645,
"initializer_range": 0.02,
"knowledge_file": "knowledge/catalog.json",
"knowledge_top_k": 8,
"max_knowledge_chars": 6000,
"model_type": "asg_transformer",
"num_scenario_labels": 8,
"pad_token_id": 151643,
"prompt_template_file": "knowledge/prompt_template.txt",
"scenario_labels": [
"awareness",
"initial_access",
"credential_protection",
"lateral_movement_detection",
"persistence_detection",
"incident_response",
"recovery",
"executive_tabletop"
],
"scenario_loss_weight": 0.1,
"semantic_loss_weight": 0.1,
"semantic_projection_dim": 384,
"transformers_version": "5.13.1",
"vocab_size": 151665
}
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