Update README.md

README.md

@@ -43,7 +43,7 @@ The tokenizer's vocabulary of this model has been expanded to 39,424, introducin
 
 The model has undergone unsupervised training on a multimodal and multilingual image-text dataset, adopting the BLIP2 Q-Former trained on a larger foundational LLM Vicuna 13B. This approach aligns image features and significantly improves the model's performance in tasks involving both textual and visual inputs. (Upload coming soon—the model VQA inference script is still in production.)
 
-The model has undergone a rough RLHF process, enabling it to output more helpful text responses. In most cases. This may increase the model's hallucination and toxicity, but it also boosts its usefulness.
+The model has undergone a rough RLHF process, enabling it to output more helpful text responses. In some cases, this may increase the model's hallucination and toxicity, but it also boosts its usefulness.
 
 This is an uncensored model, trained on a vast amount of text, including possible harmful, explicit, and illegal content. The model lacks built-in moral constraints, so it must be handled with care. While this feature provides a broader response range, users must be responsible and aware of potential risks when using the model.