Token Classification
Transformers
Safetensors
PyTorch
English
qwen2
text-generation
custom-model
text-generation-inference
Instructions to use zeltera/SMITH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeltera/SMITH with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="zeltera/SMITH")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zeltera/SMITH") model = AutoModelForCausalLM.from_pretrained("zeltera/SMITH") - Notebooks
- Google Colab
- Kaggle
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📚 Ethical and Safe Use
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SMITH is intended for defensive cybersecurity and threat intelligence purposes. It should not be used to generate or assist in creating malware, malicious code, or harmful artifacts. Users should comply with all relevant laws and organizational policies.
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📜 Citation
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If you use this model in research or deployment:
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@misc{smith2025,
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title={SMITH — Static Malware Interpreter & Threat Heuristic},
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author={Zeltera},
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year={2025},
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howpublished={Hugging Face Model},
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note={\url{https://huggingface.co/zeltera/SMITH}}
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}
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📚 Ethical and Safe Use
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SMITH is intended for defensive cybersecurity and threat intelligence purposes. It should not be used to generate or assist in creating malware, malicious code, or harmful artifacts. Users should comply with all relevant laws and organizational policies.
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