Update README.md

Files changed (1) hide show

README.md CHANGED Viewed

@@ -52,7 +52,7 @@ Optimized with **LaserRMT**
 - **License:** Apache 2.0
 - **Contact:** [Website VAGO solutions](https://vago-solutions.de/#Kontakt), [Website Hyperspace.ai](https://hyperspace.ai/)
-### Proceed for training:
 Anyone who has attempted or succeeded in fine-tuning a model is aware of the difficulty in nudging it towards a specific skill, such as mastering new languages, as well as the challenges associated with achieving significant improvements in performance. Experimenting with a novel training strategy and Spherical Linear Interpolation alongside a lasered version of the model itself has proven to be both fascinating and revealing.
 Furthermore, we developed one iteration of the model using our entire SFT -Sauerkraut dataset and two additional iterations using subsets of the full dataset—one focused on enhancing MMLU and TQA capabilities, and the other on boosting GSM8K and Winogrande skills.

 - **License:** Apache 2.0
 - **Contact:** [Website VAGO solutions](https://vago-solutions.de/#Kontakt), [Website Hyperspace.ai](https://hyperspace.ai/)
+### Training procedure:
 Anyone who has attempted or succeeded in fine-tuning a model is aware of the difficulty in nudging it towards a specific skill, such as mastering new languages, as well as the challenges associated with achieving significant improvements in performance. Experimenting with a novel training strategy and Spherical Linear Interpolation alongside a lasered version of the model itself has proven to be both fascinating and revealing.
 Furthermore, we developed one iteration of the model using our entire SFT -Sauerkraut dataset and two additional iterations using subsets of the full dataset—one focused on enhancing MMLU and TQA capabilities, and the other on boosting GSM8K and Winogrande skills.