Update README.md

README.md

@@ -7,6 +7,42 @@ language:
 tags:
 - code
 pretty_name: CVE-LLm-Dataset
-data_source: Custom data collected from CVE database
+data_source: Custom data collected from the CVE database
 data_formats: JSONL
 ---
+# CVE-llm_dataset
+This dataset is intended to train an LLM model for an utterly CVE-focused input and output.
+
+## Data extraction:
+For the data extraction, I first downloaded the CVE database from NVD lists and then loaded them using the `cve_dataset_2.py` and `cve_dataset.py` both have produce different datasets one is for llama and the other is for openai GPT.
+
+The CVE json files are mapped in this format:
+```
+cves:
+|
+├─1999
+|   ├─0xxx
+|   |   ├─CVE-1999-0001.json
+|   |   └─CVE-1999-0999.json
+|   └─1xxx
+|      ├─CVE-1999-1000.json
+|      └─CVE-1999-1598.json
+└─2023
+
+``` 
+The programs traverse through these folders extract the data in the files and arrange them into usable formats for the fine-tuning process.
+
+## llama2 Model dataset:
+The llama2 fine-tuned dataset follows this format:
+```
+    {
+        "instruction": "Explain CVE-1999-0001",
+        "input": "Explain the vulnerability: CVE-1999-0001",
+        "output": "ip_input.c in BSD-derived TCP/IP implementations allows remote attackers to cause a denial of service (crash or hang) via crafted packets.\nAffected Products: n/a\nReferences: [{'tags': ['x_refsource_CONFIRM'], 'url': 'http://www.openbsd.org/errata23.html#tcpfix'}, {'name': '5707', 'tags': ['vdb-entry', 'x_refsource_OSVDB'], 'url': 'http://www.osvdb.org/5707'}]\nCVE State: PUBLISHED"
+    }
+```
+The instruction is what we instruct the AI to do with the data provided For example we can command the AI `To take in user input analyze it and then based on what he asks returns an answer` This is also where we can add a `role` or a `personal` to the AI.
+
+The input is the user Input of the main query or data that must be processed by the AI. This is a crucial piece of information that the AI will process in order to provide an output.
+
+The output is the format that we define and tell the AI to generate answers in that format or provide that answer to the question asked.