Update README.md

README.md

@@ -16,7 +16,7 @@ datasets:
 
 # OpenOrca x OpenChat - Preview2 - 13B
 
-We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune Llama2-13B using [OpenChat](https://huggingface.co/openchat) packing and conditional behavior cloning.
+We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune Llama2-13B using [OpenChat](https://huggingface.co/openchat) packing.
 This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707).
 
 This second preview release is trained on a curated filtered subset of most of our GPT-4 augmented data.
@@ -28,7 +28,7 @@ As well, this is done with <1/10th the compute requirement and using <20% of the
 We have run extensive evaluations internally and expect this model to **place number 1** on both the HuggingFaceH4 Open LLM Leaderboard and the GPT4ALL Leaderboard for 13B models.
 
 "One" of [OpenChat](https://huggingface.co/openchat) has joined our team, and we'd like to provide special thanks for their training of this model!
-We have utilized OpenChat conditional behavior cloning and [MultiPack algorithm](https://github.com/imoneoi/multipack_sampler) which achieves 99.85% bin-packing efficiency on our dataset.
+We have utilized OpenChat [MultiPack algorithm](https://github.com/imoneoi/multipack_sampler) which achieves 99.85% bin-packing efficiency on our dataset.
 This has significantly reduced training time, with efficiency improvement of 3-10X over traditional methods.