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README.md

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-license: mit
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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 ...