TUDB-Labs
/

alpaca-mixlora-7b

Text Generation

Inference Endpoints

Model card Files Files and versions Community

mikecovlee commited on Apr 5

Commit

c28e12d

•

1 Parent(s): 345be52

Update README.md

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -5,7 +5,7 @@ datasets:
 ---
 # MixLoRA: Resource-Efficient Model with Mix-of-Experts Architecture for Enhanced LoRA Performance
-<div align="left"><img src="./assets/MixLoRA.png" width=60%"></div>
 Large Language Models (LLMs) have showcased exceptional performance across a wide array of Natural Language Processing (NLP) tasks. Fine-tuning techniques are commonly utilized to tailor pre-trained models to specific applications. While methods like LoRA have effectively tackled GPU memory constraints during fine-tuning, their applicability is often restricted. On the other hand, Mix-of-Expert (MoE) models, such as Mixtral 8x7B, demonstrate remarkable performance while maintaining a reduced parameter count. However, the resource requirements of these models pose challenges, particularly for consumer-grade GPUs.

 ---
 # MixLoRA: Resource-Efficient Model with Mix-of-Experts Architecture for Enhanced LoRA Performance
+<div align="left"><img src="MixLoRA.png" width=60%"></div>
 Large Language Models (LLMs) have showcased exceptional performance across a wide array of Natural Language Processing (NLP) tasks. Fine-tuning techniques are commonly utilized to tailor pre-trained models to specific applications. While methods like LoRA have effectively tackled GPU memory constraints during fine-tuning, their applicability is often restricted. On the other hand, Mix-of-Expert (MoE) models, such as Mixtral 8x7B, demonstrate remarkable performance while maintaining a reduced parameter count. However, the resource requirements of these models pose challenges, particularly for consumer-grade GPUs.