---
license: mit
---

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

## Description

This repository contains LoRA (Low-Rank Adaptation) adapter weights for fine-tuning a [Gemma 7B](https://huggingface.co/google/gemma2_9b_en) 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](https://huggingface.co/google/gemma2_9b_en) 
- **Fine-tuning method:** LoRA ([https://arxiv.org/abs/2106.09685](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:

1. **Load the Base Gemma Model:** Use KerasNLP to load the `google/gemma2_9b_en` base model.
2. **Enable LoRA:** Use KerasNLP's LoRA functionality to enable adapters on the appropriate layers of the Gemma model.
3. **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. 
4. **Integration:** Integrate this custom loading process into your Hugging Face Transformers-based code.

**Example Code Structure (Conceptual):**

```python
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 ...