Update README.md

README.md

@@ -2,7 +2,33 @@
 license: apache-2.0
 ---
 
-### Citation Information
+## Model Description
+
+- **Fine-Tuned from Model:** [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
+- **Paper:** [Efficient Safety Retrofitting Against Jailbreaking for LLMs](https://arxiv.org/abs/2502.13603)
+- **Point of Contact:** [Adrián Tormos](mailto:[email protected])
+
+
+## Model Summary
+
+This is a fine-tuned Llama-3.1-70B-Instruct model on the [Egida-DPO-Llama-3.1-70B-Instruct](http://huggingface.co/datasets/HPAI-BSC/Egida/viewer/Egida-DPO-Meta-Llama-3.1-70B-Instruct) dataset.
+
+The [Egida](https://huggingface.co/datasets/HPAI-BSC/Egida/viewer/Egida?views%5B%5D=egida_full) dataset is a collection of adversarial prompts that are thought to ellicit unsafe behaviors from language models. Specifically for this case, the Egida train split is used to run inference on Qwen2.5-7B-Instruct. Unsafe answers are selected, and paired with safe answers to create a customized DPO
+dataset for this model. This results in a DPO dataset composed by triplets < ”question”, ”chosen answer”, ”discarded answer” > which contain questions that elicit unsafe responses by this target model, as well as the unsafe responses produced by it.
+
+## Training Details
+
+- **Hardware:** NVIDIA H100 64 GB GPUs
+- **Devices:** 64 GPUs (16 node)
+- **Time:** 10.23h
+- **Batch Size:** 64
+- **LR:** 10−6
+
+## Environmental Impact
+
+
+## Citation Information
+
 
 ```
 @misc{garciagasulla2025efficientsafetyretrofittingjailbreaking,