Update README.md with distillation + finetuning colab notebook #41
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Added example "Distillation + Finetuning" under subheading Finetuning. The example distills the capabilities of a large model into a small model using Together's finetuning API. Details: I demonstrate how to use Curator to distill capabilities from a large language model to a much smaller 8B parameter model. I use Yelp restaurant reviews dataset to train a sentiment analysis model. I then generate a synthetic dataset using Bespokelabs's curator and finetune a model using Together's finetuning API. The finetuned model shows a 12% improvement in accuracy while being 14x cheaper than LLM
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Added example "Distillation + Finetuning" under subheading Finetuning. The example distills the capabilities of a large model into a small model using Together's finetuning API. Details: I demonstrate how to use Curator to distill capabilities from a large language model to a much smaller 8B parameter model. I use Yelp restaurant reviews dataset to train a sentiment analysis model. I then generate a synthetic dataset using Bespokelabs's curator and finetune a model using Together's finetuning API. The finetuned model shows a 12% improvement in accuracy while being 14x cheaper than LLM