add acknowledgments, citation, blog url

README.md

@@ -17,7 +17,7 @@ documents coming from 84 CommonCrawl snapshots and processed using
 the [CCNet](https://github.com/facebookresearch/cc_net) pipeline. Out of these, there are 30B documents in the corpus
 that additionally come with quality signals, and 20B documents that are deduplicated.
 
-Check out our [blog post](XXXXX) for more details on the build process, dataset structure and schema.
+Check out our [blog post](https://together.ai/blog/redpajama-data-v2) for more details on the build process, dataset structure and schema.
 
 To familiarize yourself with the dataset, you can load the sample dataset using:
 
@@ -344,10 +344,10 @@ deduplicated based on the text, using a Bloomfilter. The duplicates were kept in
 
 ## Citation
 
-To cite RedPajama-V2, please use:
+To cite RedPajama, please use:
 
 ```
-@software{together2023redpajama-v2,
+@software{together2023redpajama,
   author = {Together Computer},
   title = {RedPajama: an Open Dataset for Training Large Language Models},
   month = October,
@@ -357,8 +357,11 @@ To cite RedPajama-V2, please use:
 ```
 
 ## Acknowledgements
+We are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM models. 
+- Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data quality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and the over 500 models built on RedPajam to date by the open-source AI community.
+- We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for open-sourcing code we use in training some of the RedPajama models. 
+- Thank you to our partners of RedPajama-v1, including Ontocord.ai, MILA Québec AI Institute, ETH DS3Lab, Université de Montréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
 
--- TODO --
 
 ## License