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:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4tokenizer = AutoTokenizer.from_pretrained("madhan2301/gemma-Instruct-Finetune-on-alpaca")
5model = AutoModelForCausalLM.from_pretrained(
6 "madhan2301/gemma-Instruct-Finetune-on-alpaca",
7 device_map="auto",
8 torch_dtype=torch.bfloat16
9)
10
11input_text = "Write me a poem about Machine Learning."
12input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
13
14outputs = model.generate(**input_ids)
15print(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]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
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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