README.md

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license: apache-2.0
language:
- en
pipeline_tag: text-classification
---

# Fine-tune and Train a custom dataset for sentiment_analysis on top of Vicuna
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

#Fine-tuned Vicuna model for sentiment analysis, trained using dataset from Kaggle: 
https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp

BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF"

LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" 

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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.

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### 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)

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### Model Hub: https://huggingface.co/RinInori/vicuna_finetuned_6_sentimentsis/blob/main/images/SaveModel_Tokenizer_To_Huggingface.jpg?raw=true)

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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