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Willie999
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Model Details
Model Provider
Willie999
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
The model is intended for defensive application security engineering. Direct use-cases include:
trapSTAR can be integrated downstream as a specialized backend engine for:
This model is built strictly under dual-use protection and defensive safety policies. Out-of-scope and prohibited activities include:
Users must execute all suggested patches inside sandboxed staging environments. Security teams should treat the model's output as an assistive recommendation rather than an absolute source of truth.
You do not need to manually format or inject raw ChatML tokens into your input strings. The Hugging Face pipeline architecture parses the structural text array dynamically. Use the code snippet below to run inference:
python
import torchimport peft from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "Willie999/trapSTAR-gemma4" print("Loading tokenizer...")tokenizer = AutoTokenizer.from_pretrained(model_id) print("Configuring 4-bit VRAM compression matrix...") quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True) print("Loading model directly to CUDA memory map with 4-bit optimization...") model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda:0", quantization_config=quantization_config) # Set model to evaluation modemodel.eval() # Structure the prompt using the standard Chat template formatmessages = [ { "role": "system", "content": "You are TrapStar, an autonomous defensive security auditing agent. Analyze the provided code snippet, identify the vulnerability type, and write out structural recommendations." }, { "role": "user", "content": """Review this function block for potential vulnerabilities: ```cppvoid process_str(char *str) { char buffer[16]; strcpy(buffer, str);}```""" }] print("\nProcessing chat template serialization...")prompt_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt_text, return_tensors="pt").input_ids.to("cuda:0") print("Executing direct tensor generation with expanded token limits...")with torch.no_grad(): generated_ids = model.generate( input_ids, max_new_tokens=1536, min_new_tokens=64, temperature=0.2, do_sample=True, pad_token_id=tokenizer.eos_token_id ) # Slice away the prompt tokens so you only decode trapSTAR's specific responseresponse_tokens = generated_ids[0][input_ids.shape[-1]:]response_text = tokenizer.decode(response_tokens, skip_special_tokens=True) print("\n=== Trap Star Defense Output ===")print(response_text)import torchimport peft from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "Willie999/trapSTAR-gemma4" print("Loading tokenizer...")tokenizer = AutoTokenizer.from_pretrained(model_id) print("Configuring 4-bit VRAM compression matrix...") quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True) print("Loading model directly to CUDA memory map with 4-bit optimization...") model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda:0", quantization_config=quantization_config) # Set model to evaluation modemodel.eval() # Structure the prompt using the standard Chat template formatmessages = [ { "role": "system", "content": "You are TrapStar, an autonomous defensive security auditing agent. Analyze the provided code snippet, identify the vulnerability type, and write out structural recommendations." }, { "role": "user", "content": """Review this function block for potential vulnerabilities: ```cppvoid process_str(char *str) { char buffer[16]; strcpy(buffer, str);}```""" }] print("\nProcessing chat template serialization...")prompt_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt_text, return_tensors="pt").input_ids.to("cuda:0") print("Executing direct tensor generation with expanded token limits...")with torch.no_grad(): generated_ids = model.generate( input_ids, max_new_tokens=1536, min_new_tokens=64, temperature=0.2, do_sample=True, pad_token_id=tokenizer.eos_token_id ) # Slice away the prompt tokens so you only decode trapSTAR's specific responseresponse_tokens = generated_ids[0][input_ids.shape[-1]:]response_text = tokenizer.decode(response_tokens, skip_special_tokens=True) print("\n=== Trap Star Defense Output ===")print(response_text)