Update README.md

README.md

@@ -47,9 +47,11 @@ Introducing **SauerkrautLM-1.5b** – our Sauerkraut version of the powerful [Qw
 - **Contact:** [VAGO solutions](https://vago-solutions.ai)
 
 ## Training Procedure
+
 This model is a demo intended to showcase the potential of resource-efficient training of large language models using Spectrum CPT. Here's a brief on the procedure:
 
 **Continuous Pre-training (CPT) on German Data**: 
+
 Utilizing Spectrum by Eric Hartford, Lucas Atkins, Fernando Fernandes Neto, and David Golchinfar, the model targeted 25% of its layers during training. This approach allowed significant resource savings:
 Spectrum with 25% layer targeting consumed 309.78GB at a batch size of 2048.
 Full Fine-tuning targeting 100% of layers used 633.55GB at the same batch size.
@@ -62,8 +64,10 @@ In the German Rag evaluation, it is on par with 8 billion parameter models and,
 Despite the large volume of German CPT data, the model competes well against the Qwen2-1.5B-Instruct model and performs significantly better in German.
 
 **Post-CPT Training**: 
+
 The model underwent 3 epochs of Supervised Fine-Tuning (SFT) with 700K samples.
 **Further Steps**: 
+
 The model was aligned with Direct Preference Optimization (DPO) using 70K samples.
 
 ## Objective and Results