Instructions to use madox81/SmolLM2-Cyber-Insight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madox81/SmolLM2-Cyber-Insight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="madox81/SmolLM2-Cyber-Insight") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("madox81/SmolLM2-Cyber-Insight") model = AutoModelForCausalLM.from_pretrained("madox81/SmolLM2-Cyber-Insight") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use madox81/SmolLM2-Cyber-Insight with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "madox81/SmolLM2-Cyber-Insight" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "madox81/SmolLM2-Cyber-Insight", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/madox81/SmolLM2-Cyber-Insight
- SGLang
How to use madox81/SmolLM2-Cyber-Insight 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 "madox81/SmolLM2-Cyber-Insight" \ --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": "madox81/SmolLM2-Cyber-Insight", "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 "madox81/SmolLM2-Cyber-Insight" \ --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": "madox81/SmolLM2-Cyber-Insight", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use madox81/SmolLM2-Cyber-Insight with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for madox81/SmolLM2-Cyber-Insight to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for madox81/SmolLM2-Cyber-Insight to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for madox81/SmolLM2-Cyber-Insight to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="madox81/SmolLM2-Cyber-Insight", max_seq_length=2048, ) - Docker Model Runner
How to use madox81/SmolLM2-Cyber-Insight with Docker Model Runner:
docker model run hf.co/madox81/SmolLM2-Cyber-Insight
Uploaded finetuned model
- Developed by: madox81
- License: apache-2.0
- Finetuned from model : unsloth/SmolLM2-1.7b-Instruct
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Smollm2_Cyber_Insight
Model Overview
Smollm2_Cyber_Insight is a lightweight domain-adapted language model fine-tuned for cybersecurity threat analysis tasks.
The model specializes in interpreting short textual descriptions of security incidents and producing structured (JSON) security insights.
- Base Model: smollm2-1.7b-instruct
- Architecture: SmolLM2
- Training Method: LoRA fine-tuning
- Domain: Cyber Threat Analysis
- Model Size: ~1.7B parameters
Capabilities
The model supports the following tasks:
- Mapping incidents to MITRE ATT&CK tactics
- Identifying possible attack techniques
- Assessing incident severity and potential business impact
- Assisting in structured cybersecurity analysis
Intended Use
This model is suitable for:
- Cyber threat intelligence experiments
- NLP research in cybersecurity
- Cybersecurity research
- Prototyping AI-assisted SOC tools
Limitations
- Predictions are probabilistic and may require analyst validation
- Performance depends on similarity to training data
- Not intended for autonomous security decision-making
Training Data
The model was trained on a specialized cybersecurity dataset madox81/mittre_severity_ds containing incident descriptions and structured labels including:
- attack tactics
- attack techniques
- incident severity indicators.
Example Prompt
Map the following security event to MITRE ATT&CK tactics and techniques.
Input: rule apt_lolbin { strings: $a = "certutil.exe" nocase; $b = "-urlfetch" nocase; condition: $a and $b }
Identify the ATT&CK tactics and techniques in this data.
Input: selection: EventName: 'UpdateDomainNameservers' AND SourceIPAddress not in ('aws-internal')
Classify this cybersecurity event into MITRE ATT&CK framework.
Input: rule apt_wasm { strings: $a = "WebAssembly.compile" nocase; $b = "fetch" nocase; condition: $a and $b }
Map the following security event to MITRE ATT&CK tactics and techniques.
Input: Incident Type: Data Breach
Target: MongoDB Instance
Vector: Weak Authentication
Assess the severity and business risk of the following incident.
Input: Incident: Phishing affecting HR Accounts.
Analyze the business risk and severity for the input below.
Input: Incident: Supply Chain Attack affecting CI/CD Pipeline.
Rate the severity (Low/Medium/High/Critical) and impact of this event.
Input: Incident: Credential Dumping affecting Windows Domain Controller.
License
Refer to the base model license.
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