Duplicate from sushanrai/phi3-cybersec-advisor-lora

Co-authored-by: Sushan Rai <sushanrai@users.noreply.huggingface.co>

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README.md

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+# Phi-3 Mini (LoRA Fine-Tuned on MITRE-STIX-CVE-ExploitDB Dataset)
+
+## Model Summary
+
+This model is a fine-tuned version of **[microsoft/phi-3-mini-128k-instruct](https://huggingface.co/microsoft/phi-3-mini-128k-instruct)** using **LoRA (Low-Rank Adaptation)** and **8-bit quantization** for parameter-efficient training.
+
+The fine-tuning dataset is **[jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca](https://huggingface.co/datasets/jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca)**, which contains security-related instruction-response examples (CVE, STIX, ExploitDB context).
+
+The goal of this model is to act as a **cybersecurity knowledge assistant** that can answer questions about CVEs, exploits, and related security topics.
+
+* **Base Model**: microsoft/phi-3-mini-128k-instruct
+* **Fine-tuning Method**: LoRA (8-bit PEFT with bitsandbytes)
+* **Dataset**: jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca
+* **Languages**: English
+* **Context Length**: 128k tokens
+
+---
+
+## Intended Uses
+
+* Designed for: cybersecurity Q\&A, reasoning about vulnerabilities, exploits, and threat intelligence.
+* Can be used for: research, learning, and prototyping of cyber threat assistants.
+
+---
+
+## Dataset
+
+* **Name**: MITRE-STIX-CVE-ExploitDB Dataset (Alpaca format)
+* **Source**: [jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca](https://huggingface.co/datasets/jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca)
+* **Schema**: Instruction–response pairs in Alpaca format
+* **Size Used**: Up to 5,000 training samples (subset for efficiency)
+
+---
+
+## Training Procedure
+
+* **Frameworks**: Hugging Face Transformers, PEFT, bitsandbytes
+* **Precision**: 8-bit quantization (bnb.int8) + FP16 training
+* **Optimizer**: AdamW
+* **Batch Size**: 4 per device
+* **Epochs**: 1
+* **Learning Rate**: 3e-4
+* **Warmup Steps**: 50
+* **Max Length**: 1024 tokens
+* **LoRA Config**:
+
+  * r = 16
+  * alpha = 16
+  * dropout = 0.05
+  * target modules: q\_proj, k\_proj, v\_proj, o\_proj, w1, w2, dense
+
+---
+
+## Evaluation
+
+* **Metric**: Training loss (did not include a validation set in this run).
+* **Qualitative Evaluation**: The model produces meaningful responses to security-related prompts, but further fine-tuning with eval sets is recommended.
+
+---
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_name = "sushanrai/phi3-cybersec-advisor-lora"  # replace with your repo
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+prompt = "Explain CVE-2021-44228 in simple terms"
+output = pipe(prompt, max_new_tokens=300, do_sample=True)
+print(output[0]["generated_text"])
+```
+
+---
+
+## Ethical Considerations
+
+* This model is trained on cybersecurity data and may produce outputs that describe exploits.
+* Should only be used for **research, learning, and defensive security purposes**.
+* Not intended for malicious use.
+
+---
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{phi3_mitre_lora_2025,
+  title={Phi-3 Mini LoRA Fine-Tuned on MITRE-STIX-CVE-ExploitDB Dataset},
+  author={HackDMSV},
+  year={2025},
+  howpublished={\url{https://huggingface.co/sushanrai/phi3-cybersec-advisor-lora}}
+}
+```

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