Update README.md

README.md

@@ -121,16 +121,6 @@ You can automatically apply it using the dedicated [`.apply_chat_template()`](ht
 
 RAG systems enable AI solutions to include new, up-to-date, and potentially proprietary information in LLM responses that was not present in the training data. When a user asks a question, the retrieval component locates and delivers related documents from a knowledge base, and then the RAG generator model answers the question based on facts from those contextual documents.
 
-## 📈 Performance
-
-We evaluated the model across 3 metrics using LLMs as a judge, comparing against 4 similarly-sized open-source models:
-
-- **Groundedness**: Do the model’s responses consist entirely of information from the provided contextual documents and avoid hallucinations?
-- **Relevance**: Does the model answer the user’s question concisely? Does all of the response content contribute to the final answer without inclusion of unnecessary fluff?
-- **Helpfulness**: Overall, how well did the model assist with the user’s query?
-
-LFM2-1.2B-RAG achieves competitive performance in all 3 metrics compared to Qwen3-1.7B, Gemma3-1B-IT, Llama-3.21B-Instruct, and Pleias-1B-RAG.
-
 ## 🏃 How to run
 
 - Hugging Face: [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)