Update README.md

README.md

@@ -23,10 +23,18 @@ High quality datasets are the powerhouse of LLMs and require special care and sk
 
 The datatsets and training recipes of most state-of-the-art models are not released. We build cutting-edge models and release the full training pipeline as well fostering more innovation and reproducibility, such as [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), or [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct).
 
+### 🌸 Collaborations
+
+Research and collaboration go hand in hand. That's why we like to organize and participate in large open collaborations such as [BigScience](https://bigscience.huggingface.co) and [BigCode](https://www.bigcode-project.org). 
+
 ### ⚙️ Infrastructre
 
 The research team is organized in small teams with typically <4 people and the science cluster consists of 96 x 8xH100 nodes as well as an auto-scalable CPU cluster for dataset processing. In this setup, even a small research team can build and push out impactful artifacts.
 
+### 📖 Educational material
+
+Besides writing tech reports of research projects we also like to write more educational content to help newcomers get started to the field or practitioners. We built for example the [alignment handbook](https://github.com/huggingface/alignment-handbook), the [pretraining tutorial](https://www.youtube.com/watch?v=2-SPH9hIKT8), or the [FineWeb blog](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1).
+
 ### 🚀 Releases
 
 This is the release timeline of 2024 so far (you can click on each element!):