Update README.md

README.md

@@ -12,33 +12,35 @@ datasets:
 - LDJnr/Verified-Camel
 ---
 
-## **Nous-Capybara-7B**
+## **Nous-Capybara-7B V1**
 
-**MUCH BETTER MISTRAL BASED VERSION COMING SOON AS CAPYBARA V2**
+**MUCH BETTER MISTRAL BASED VERSION IS OUT NOW AS CAPYBARA V1.9**
 
-A model created with a novel synthesis method in mind, Amplify-instruct, with a goal of having a synergistic combination of different techniques used for SOTA models such as Evol-Instruct, Orca, Vicuna, Lamini, FLASK and others, all into one lean holistically formed dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly acclaimed 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 multi-turn datasets like Dove(A successor to Puffin).
+The Capybara series is made by fine-tuning on data that is created by Nous with our novel data synthesis technique called Amplify-instruct, 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, Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed 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).
 
-Entirely contained under 20K training examples, mostly comprised of newly synthesized tokens never used for model training until now!
+While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, mostly comprised of newly synthesized conversation tokens that have never previously been used for AI training to our knowledge.
+
+This small fine-tune dataset has significant implications for how we'll be able to scale model abilities in the future! This model is currently 20K examples while matching benchmarks of notable 300K example datasets that are 10 times the size!
 
 ## Process of creation and special thank yous!
 
 This model was fine-tuned by Nous Research, with LDJ leading the training and dataset curation, along with significant dataset formation contributions by J-Supha, Also thank you to Emozilla for also assisting to expedite the training experimentation process.
 
-Special thank you to **A16Z** for sponsoring our training, as well as  **Yield Protocol** for their support in resources during R&D of aspects outside of training, such as dataset development/synthesis.
+Special thank you to **A16Z** for sponsoring our training, as well as **Yield Protocol** for their support in resources during R&D of aspects outside of training, such as dataset development/synthesis.
+
+## Thank you to those of you that have indirectly contributed!
 
-## Thank you to dataset creators!
+While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to generate the multi-turn data as part of the Amplify-Instruct synthesis.
 
-While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to initiate the beggining of many of the multi-turn conversations:
+The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project.
 
 ![Capybara](https://i.imgur.com/yB58OoD.jpeg)
 
 ## Model Training
 
-Nous-Capybara 7B is a new model trained for multiple epochs on a dataset of less than 20,000 carefully curated GPT-4 examples, most of which is comprised of entirely newly synthesized tokens that previously didn't exist on HuggingFace.
-
-Additional data came from manually curated CamelAI data, with the help of volunteers ranging from former Physicists, Mathematicians, Biologists and more! 
+Nous-Capybara 7B is a new model trained for multiple epochs on a dataset of roughly 20,000 carefully curated conversational examples, most of which are comprised of entirely new in-house synthesized tokens that previously didn't exist on HuggingFace.
 
-Specific credits to the people involved in validating this data will be posted soon :)
+Additional data came from manually curated CamelAI data, with the help of volunteers ranging from former Physics PhD's, Mathematicians, Biologists and more! 
 
 ## Prompt Format
 
@@ -54,15 +56,13 @@ ASSISTANT:
 
  - The first Nous model trained on over 10,000 multi-turn conversations.
 
- - Over 1,000 tokens average per conversation example during training!
+ - Over 1,000 tokens average per conversation example and multiple back and forth turns per conversation! Most models are still trained for only single-turn conversations and less than 300 tokens per example!
 
- - Able to effectively do complex summary of advanced studies on topics.
+ - Able to effectively do complex summaries of advanced topics and studies.
 
- - Ability to recall information upto late 2022 without internet (ChatGPT cut off date is in 2021)
+ - Ability to recall information upto late 2022 without internet.
 
- - Context length of 4096 tokens, and fine-tuned on a significant amount of multi-turn conversations reaching that full token limit.
-
- - Includes a portion of conversational data synthesized from less wrong posts, speaking in-depth about the nature of rationality, reasoning and self-improvement.
+ - Includes a portion of conversational data synthesized from less wrong posts, discussing very in-depth about the nature of rationality, reasoning, self-improvement and related concepts.
 
 ## Example Outputs!:
 
@@ -72,19 +72,16 @@ ASSISTANT:
 
 ![Capybara](https://img001.prntscr.com/file/img001/85X3L9ZxTsOKo3fUQ7GRVA.png)
 
-## Benchmarks! (Important to note that all mentioned benchmarks are single-turn and don't test multi-turn capabilities, Capybara should excel even further at multi-turn conversational tasks.)
+## Benchmarks! (Important to note that all mentioned benchmarks are single-turn and don't test multi-turn capabilities, Capybara should excel even further at multi-turn conversational tasks than what benchmark comparisons show.)
 
 ![Capybara](https://i.imgur.com/n8lkmyK.png)
  
-## Limitations
-
-We noticed that the current version of Capybara still has some issues in some situations with censoring itself and not acting as expected in certain edge cases, we plan to have this largely resolved in the near future with Capybara 1.1
 
 ## Future Changes
 
 This is a relatively early build amongst the grand plans for the future of Capybara! 
 
-Current limitations: We are still running experimentation and tests for the training pipeline and dataset cleaning process to be more refined, we plan to release a Capybara 1.1 with these improvements.
+[IT IS NOW RECCOMENDED TO USE CAPYBARA V1.9 FOR SIGNIFICANTLY BETTER OVERALL CAPABILITIES]
 
 ## Future model sizes