3,500 cybersecurity instruction pairs were used to fine-tune a Llama3.1 8B model via PEFT/LoRA. The dataset consists of adapter weights for the model. It was uploaded to Kaggle, but the author and other metadata are unspecified.
Use Cases
- Generate cybersecurity threat intelligence reports based on the fine-tuned model's knowledge.
- Answer technical questions about vulnerabilities and exploits based on the instruction-tuning data.
- Assist in security code review by identifying potential flaws based on learned patterns.
- Simulate phishing or social engineering attack scenarios for training purposes based on the model's training.
Strengths
- Contains adapter weights fine-tuned on 3,500 specific instruction-response pairs.
- Focuses on the cybersecurity domain, which may provide specialized utility.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- kaggle
- Collection Method
- Fine-tuned on instruction pairs.