Update README.md

README.md

@@ -5,6 +5,9 @@ language:
 pipeline_tag: text-classification
 ---
 
+# Vicuna_finetune_sentiment_analysis
+Vicuna_finetune_sentiment_analysis through PEFT and LoRA.
+
 To Run the app: https://huggingface.co/spaces/RinInori/vicuna_finetuned_6_sentiments?logs=build
 
 Github fine-tune code link: https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis
@@ -16,4 +19,34 @@ BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF"
 
 LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" 
 
+---
+
+Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from ShareGPT.com with public APIs.
+To find more about Vicuna here: https://lmsys.org/blog/2023-03-30-vicuna/
+
+To train a custom dataset on top of Vicuna if we don’t have good access to data-center grade GPU, is to fine-tune it through PEFT and LoRA.
+
+PEFT = parameter-Efficient Fine_Tuning of Billion-Scale Models on Low-Resource hardware.
+
+LoRA = Low-Rank Adaptation of Large Language Models is a training method that accelerates the training of large models while consuming less memory. 
+It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights.
+
+---
+
+### To RUN APP: https://huggingface.co/spaces/RinInori/vicuna_finetuned_6_sentiments
+
+![Image description](https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis/blob/main/vicuna_result_correct.jpg?raw=true)
+
+-----------------
+### Model Hub: https://huggingface.co/RinInori/vicuna_finetuned_6_sentiments
+
+![Image description](https://github.com/hennypurwadi/Bert_FineTune_Sentiment_Analysis/blob/main/images/SaveModel_Tokenizer_To_Huggingface.jpg?raw=true)
+---
+
+Ref: https://www.youtube.com/watch?v=Us5ZFp16PaU 
+
+Ref: https://arxiv.org/abs/2106.09685
+
+Ref: https://huggingface.co/docs/diffusers/training/lora#lowrank-adaptation-of-large-language-models-lora
 
+Ref: Hutchinson, B., Ostendorf, M., & Fazel, M. (2011, September). Low Rank Language Models for Small Training Sets. IEEE Signal Processing Letters, 18(9), 489–492. https://doi.org/10.1109/lsp.2011.2160850