Update README.md

README.md

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 **This is trained on the Yi-34B model with 200K context length, for 3 epochs on the Capybara dataset!**
 
+**First 34B Nous model and first 200K context length Nous model!**
+
 The Capybara series is the first Nous collection of models made by fine-tuning mostly on data created by Nous in-house. 
 
 We leverage our novel data synthesis technique called Amplify-instruct (Paper coming soon), the seed distribution and synthesis method are comprised of a synergistic combination of top performing existing data synthesis techniques and distributions used for SOTA models such as Airoboros, Evol-Instruct(WizardLM), Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed methodology for the dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain in-house multi-turn datasets like Dove(A successor to Puffin).