Update README.md

README.md

@@ -12,13 +12,16 @@ tags:
 
 Introducing TenyxChat-8x7B-v1, part of our TenyxChat series trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our model is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
 
-We fine-tune [Mixtral-8x7B-Instruct-v0.1](https://arxiv.org/pdf/2401.04088.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods) already applied to obtain TenyxChat-7B-v1 (https://huggingface.co/tenyx/TenyxChat-7B-v1), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685). Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-8x7B-v1 was trained using eight A100s (80GB) for about eight hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
+We fine-tune [Mixtral-8x7B-Instruct-v0.1](https://arxiv.org/pdf/2401.04088.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), 
+similar to that of our [7B model](already applied to obtain TenyxChat-7B-v1 (https://huggingface.co/tenyx/TenyxChat-7B-v1), and show an increase in [MT-Bench](https://arxiv.org/abs/2306.05685) scores. 
+Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. 
+TenyxChat-8x7B-v1 was trained using eight A100s (80GB) for about eight hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
 
  # Model details
 
 - Model type: Fine-tuned Mixture Of Expert 8x7B model for chat.
 - License: Apache 2.0
-- Base model: Mixtral-8x7B-Instruct-v0.1  ([https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1))
+- Base model: [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
 - Demo: [spaces/tenyx/TenyxChat-8x7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-8x7B-v1)
 
 ## Usage
@@ -60,7 +63,7 @@ outputs = pipe(prompt, max_new_tokens=512, do_sample=False)
 
 # Performance
 
-At the time of release (Jan 2024), TenyxChat-8x7B-v1 is the highest-ranked open-source model only superseded by GPT4 on the MT-Bench evaluation available for download and commercial use. 
+At the time of release (Jan 2024), TenyxChat-8x7B-v1 is the highest-ranked model, only superseded by GPT4, on the MT-Bench evaluation available for download and commercial use. 
 
 ## MT-Bench