gemma-Instruct-Finetune-on-alpaca

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Fine-Tuning Gemma Model with qLora and Supervised Fine-Tuning

This repository contains a comprehensive tutorial and notebook for fine-tuning the gemma-7b-it model using qLora and Supervised Fine-Tuning (SFT). The tutorial demonstrates the process from setting up the environment to fine-tuning the model on a code generation dataset.

Overview

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/gemma-header.width-1200.format-webp.webp" width="100%">

This notebook provides an end-to-end guide on how to fine-tune the gemma-7b-it model. The fine-tuning process includes:

1. Setting up the environment and prerequisites
2. Loading and configuring the model with QLoRA quantization
3. Preparing and formatting the dataset
4. Applying LoRA for efficient fine-tuning
5. Running the fine-tuning process
6. Testing the fine-tuned model

Prerequisites

Ensure that you have the following prerequisites before running the notebook:

• GPU: A T4 (for gemma-2b) or an A100 GPU (for gemma-7b).
• Python Packages: Install the necessary Python packages using the commands provided in the notebook.

Model Details

1. Use the following Python code snippet to generate text using the model:

   from transformers import AutoTokenizer, AutoModelForCausalLM
   import torch
   
   tokenizer = AutoTokenizer.from_pretrained("madhan2301/gemma-Instruct-Finetune-on-alpaca")
   model = AutoModelForCausalLM.from_pretrained(
       "madhan2301/gemma-Instruct-Finetune-on-alpaca",
       device_map="auto",
       torch_dtype=torch.bfloat16
   )
   
   input_text = "Write me a poem about Machine Learning."
   input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
   
   outputs = model.generate(**input_ids)
   print(tokenizer.decode(outputs[0]))
   

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [https://huggingface.co/madhan2301]
• Model type: [Instruct-Finetune-on-alpaca]
• Language(s) (NLP): [More Information Needed]
• License: [apache]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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• Repository: [huggingface.co/madhan2301/gemma-Instruct-Finetune-on-alpaca]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Software

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Glossary [optional]

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