Update README.md

README.md

@@ -16,18 +16,9 @@ In the v1.5 (08/2024) release, we present a series of XGen-MM models including:
 - [🤗 xGen-MM-instruct](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-singleimg-r-v1.5): `xgen-mm-phi3-mini-instruct-singleimg-r-v1.5`
 - [🤗 xGen-MM-instruct-dpo](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-dpo-r-v1.5): `xgen-mm-phi3-mini-instruct-dpo-r-v1.5`
 
-<! --In addition to the models, our team also released a series of datasets for multi-modal pre-training, including:
-- [🍃 MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens](https://arxiv.org/abs/2406.11271)
-- [🤗 BLIP3-OCR-200M](https://huggingface.co/datasets/Salesforce/blip3-ocr-200m): a dataset with dense OCR annotations.
-- [🤗 BLIP3-GROUNDING-50M](https://huggingface.co/datasets/Salesforce/blip3-grounding-50m): a dataset for enhancing the ability to ground semantic concepts in images.
-- BLIP3-KALE (stay tuned): a large-scale curated high-quality caption dataset. 
-
 For more details, check out our [tech report](https://arxiv.org/pdf/2408.08872), [fine-tuning code](https://github.com/salesforce/LAVIS/tree/xgen-mm), and [project page](https://www.salesforceairesearch.com/opensource/xGen-MM/index.html).
 
 
-# Data
-The instruct model is fine-tuned on a mixture of around 1 million samples from multiple domains. All the fine-tuning data are from public sources, most of which are covered in [The Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron).
--->
 # Results
 
 ### Single-image benchmarks