Model Card for Model ID

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

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

Setting up the environment and prerequisites
Loading and configuring the model with QLoRA quantization
Preparing and formatting the dataset
Applying LoRA for efficient fine-tuning
Running the fine-tuning process
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

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

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]

Repository: [huggingface.co/madhan2301/gemma-Instruct-Finetune-on-alpaca]
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Uses

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

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Training Procedure

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Summary

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Environmental Impact

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

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