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barha
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Model Details
Model Provider
barha
Model Tree
ibm-granite/granite-4.1-3b
Input Modalities
Output Modalities
Supported Functionality
Given a CTI procedure sentence, the model returns a single ATT&CK technique identifier. Prompt format:
text
What ATT&CK technique does the following CTI procedure sentence describe?<cti>{procedure sentence}</cti>python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained('ibm-granite/granite-4.1-3b', device_map='cuda')model = PeftModel.from_pretrained(base, 'barha/granite-cti-technique-mapping-lora')tok = AutoTokenizer.from_pretrained('barha/granite-cti-technique-mapping-lora') prompt = 'What ATT&CK technique does the following CTI procedure sentence describe?\n\n<cti>\nGazer can establish persistence by creating a .lnk file in the Start menu.\n</cti>'msgs = [{'role': 'user', 'content': prompt}]inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors='pt').to('cuda')out = model.generate(inputs, max_new_tokens=16)print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)) # -> T1547.001LoRA adapter only (~124 MB); load on top of the base model as shown above.
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained('ibm-granite/granite-4.1-3b', device_map='cuda')model = PeftModel.from_pretrained(base, 'barha/granite-cti-technique-mapping-lora')tok = AutoTokenizer.from_pretrained('barha/granite-cti-technique-mapping-lora') prompt = 'What ATT&CK technique does the following CTI procedure sentence describe?\n\n<cti>\nGazer can establish persistence by creating a .lnk file in the Start menu.\n</cti>'msgs = [{'role': 'user', 'content': prompt}]inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors='pt').to('cuda')out = model.generate(inputs, max_new_tokens=16)print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)) # -> T1547.001