metadata

license: mit

threatthriver/Gemma-7B-LoRA-Fine-Tuned

Description

This repository contains LoRA (Low-Rank Adaptation) adapter weights for fine-tuning a Gemma 7B model on a custom dataset of [briefly describe your dataset].

Important: This is NOT a full model release. It only includes the LoRA adapter weights and a config.json to guide loading the model. You will need to write custom code to load the base Gemma model and apply the adapters.

Model Fine-tuning Details

Base Model: google/gemma2_9b_en
Fine-tuning method: LoRA (https://arxiv.org/abs/2106.09685)
LoRA rank: 8
Dataset: [Briefly describe your dataset and provide a link if possible]
Training framework: KerasNLP

How to Use

This model release is not directly compatible with the transformers library's standard loading methods. You will need to:

Load the Base Gemma Model: Use KerasNLP to load the google/gemma2_9b_en base model.
Enable LoRA: Use KerasNLP's LoRA functionality to enable adapters on the appropriate layers of the Gemma model.
Load Adapter Weights: Load the adapter_model.bin and other relevant files from this repository to apply the fine-tuned adapter weights to the base Gemma model.
Integration: Integrate this custom loading process into your Hugging Face Transformers-based code.

Example Code Structure (Conceptual):

import keras_nlp
from transformers import GemmaTokenizerFast  # Or appropriate tokenizer

# ... Load base Gemma model using KerasNLP ...

# ... Enable LoRA adapters on target layers ... 

# ... Load adapter weights from this repository ...

# ... Use tokenizer, model for generation or other tasks ...