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---
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 ... |