Update README.md

README.md

@@ -17,23 +17,72 @@ should probably proofread and complete it, then remove this comment. -->
 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/drorrabin/cvss_base_score/runs/2a8pl3jv)
 # cvss_base_score-2025-06-28_12.55.51
 
-This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on an unknown dataset.
+# 🛡️ CVSS v3 Base Score Estimation Model
 
-## Model description
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) designed to **predict CVSS v3 base scores** based on vulnerability descriptions.
 
-More information needed
+---
+
+## 🔍 Model Details
+
+- **Base Model:** Meta-Llama 3.1 8B (4-bit QLoRA fine-tuning)  
+- **Task:** Regression-style score prediction (0.0 to 10.0)  
+- **Output Format:** The model generates a numeric CVSS base score as part of its response  
+- **Quantization:** 4-bit using QLoRA for memory-efficient fine-tuning  
+- **LoRA Config:**  
+  - `r = 32`  
+  - `alpha = 64`  
+  - `target_modules = ["q_proj", "v_proj", "k_proj", "o_proj"]`  
+  - `dropout = 0.1`  
+
+---
+
+## 📦 Intended Use
+
+This model is intended for assisting security analysts, vulnerability management platforms, or automated tools to **estimate the CVSS v3 base score** given a detailed vulnerability description.
+
+### Example Prompt
+
+What is the CVSS v3 base score of the following vulnerability
+
+CVE Description: admin/limits.php in Dolibarr 7.0.2 allows HTML injection, as demonstrated by the MAIN_MAX_DECIMALS_TOT parameter.
+
+Weakness Type: CWE-79 (Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting'))
 
-## Intended uses & limitations
+Affected Product: dolibarr_erp/crm
 
-More information needed
+Reported by: cve@mitre.org in 2022
 
-## Training and evaluation data
+The CVSS v3 base score is
+
+### ------------------------------------------------- 
+The model is expected to output the score (e.g., `5.4`).
+### ------------------------------------------------- 
+
+
+
+
+### Framework versions
+
+- PEFT 0.15.2
+- Transformers 4.52.4
+- Pytorch 2.6.0+cu124
+- Datasets 3.6.0
+- Tokenizers 0.21.2
 
-More information needed
 
-## Training procedure
 
-### Training hyperparameters
+
+---
+
+## 📊 Training Details
+
+- **Dataset:** Crafted dataset of CVE descriptions and corresponding CVSS v3 base scores  
+- **Training Framework:** Hugging Face Transformers, TRL's `SFTTrainer`, PEFT with QLoRA  
+- **Hardware:** Colab with 4-bit quantization for efficient resource usage 
+
+
+#### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0001
@@ -45,10 +94,28 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.03
 - num_epochs: 1
 
-### Framework versions
+---
+
+#### ⚠️ Limitations
+
+- The model **does not perform strict numerical regression** — it generates a number as text  
+- May produce invalid outputs if the prompt is incomplete or malformed  
+- Should not be relied upon as the sole authority for CVSS scoring — use as an assistive tool only  
+
+---
+
+## ✅ How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "your-hf-username/your-model-name"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "What is the CVSS v3 base score of the following vulnerability\n\nCVE Description: Example vulnerability ...\nThe CVSS v3 base score is "
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=10)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
-- PEFT 0.15.2
-- Transformers 4.52.4
-- Pytorch 2.6.0+cu124
-- Datasets 3.6.0
-- Tokenizers 0.21.2