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Nitesh208
Available on FriendliAI
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Model Details
Model Provider
Nitesh208
Model Tree
unsloth/Llama-3.2-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "Nitesh208/secureiq-threat-classifier"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are SecureIQ, an expert AI security analyst.<|eot_id|><|start_header_id|>user<|end_header_id|>Classify this vulnerability: Remote code execution via buffer overflow<|eot_id|><|start_header_id|>assistant<|end_header_id|>""" inputs = tokenizer(prompt, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=150)print(tokenizer.decode(outputs[0], skip_special_tokens=True))Severity: CRITICAL CVSS Score: 9.8/10 Risk Score: 95/100 Recommendation: Immediate emergency patching required. Isolate affected systems.
This model powers the threat classification layer of SecureIQ:
| Layer | Technology |
|---|---|
| RAG Knowledge Base | LlamaIndex + ChromaDB |
| Investigation Agent | LangGraph + LangChain |
| Anomaly Detection | Isolation Forest (scikit-learn) |
| Threat Classifier | This model |
| Alert System | Telegram Bot API |
| Dashboard | React + FastAPI |
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "Nitesh208/secureiq-threat-classifier"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are SecureIQ, an expert AI security analyst.<|eot_id|><|start_header_id|>user<|end_header_id|>Classify this vulnerability: Remote code execution via buffer overflow<|eot_id|><|start_header_id|>assistant<|end_header_id|>""" inputs = tokenizer(prompt, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=150)print(tokenizer.decode(outputs[0], skip_special_tokens=True))