Update README.md

README.md

@@ -8,7 +8,7 @@ license: cc-by-nc-4.0
 ## Model Details
 ICKG (Integrated Contextual Knowledge Graph Generator) v3.2 is a knowledge graph construction (KGC) task-specific instruction-following large language model (LLM) fine-tuned from [Mistral 7B](https://arxiv.org/abs/2310.06825). It outperforms the latest [ICKG v2.0](https://huggingface.co/victorlxh/ICKG-v2.0) that is fine-tuned from Vicuna LLM. 
 
-- **Developed by**: [Xiaohui Li](https://xiaohui-victor-li.github.io/)
+- **Developed by**: [Xiaohui Victor Li](https://xiaohui-victor-li.github.io/)
 - **Model type**: Auto-regressive language model based on the transformer architecture.
 - **License**: Non-commercial
 - **Finetuned from model**: [Mistral-7B](https://arxiv.org/abs/2310.06825).
@@ -26,7 +26,9 @@ The primary use of ICKG LLM is for generating knowledge graphs (KG) based on ins
 
 - Aspect-Based Sentiment Analysis (ABSA) represents a refined facet of sentiment analysis that specifically targets the sentiments associated with distinct aspects or attributes within a text. This granular approach is crucial for applications where understanding nuanced opinions about specific features is essential. ABSA not only discerns the overall sentiment of the text but also pinpoints and evaluates sentiments related to individual aspects mentioned within the document. 
 
+- ⚠️ As a prerequisite, ensure the Hugging Face python library `transformers` and the auxiliary `peft` library (https://github.com/huggingface/peft) are pre-installed.
 
+- The LLM is best suited to run on a GPU server  
 
 ## How to Get Started with the Model
 - **Python Code**: [https://github.com/xiaohui-victor-li/FinDKG](https://github.com/xiaohui-victor-li/FinDKG)