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license: mit |
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# threatthriver/Gemma-7B-LoRA-Fine-Tuned |
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## Description |
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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**]. |
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**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. |
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## Model Fine-tuning Details |
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- **Base Model:** [google/gemma2_9b_en](https://huggingface.co/google/gemma2_9b_en) |
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- **Fine-tuning method:** LoRA ([https://arxiv.org/abs/2106.09685](https://arxiv.org/abs/2106.09685)) |
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- **LoRA rank:** 8 |
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- **Dataset:** [**Briefly describe your dataset and provide a link if possible**] |
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- **Training framework:** KerasNLP |
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## How to Use |
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This model release is not directly compatible with the `transformers` library's standard loading methods. You will need to: |
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1. **Load the Base Gemma Model:** Use KerasNLP to load the `google/gemma2_9b_en` base model. |
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2. **Enable LoRA:** Use KerasNLP's LoRA functionality to enable adapters on the appropriate layers of the Gemma model. |
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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. |
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4. **Integration:** Integrate this custom loading process into your Hugging Face Transformers-based code. |
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**Example Code Structure (Conceptual):** |
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```python |
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import keras_nlp |
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from transformers import GemmaTokenizerFast # Or appropriate tokenizer |
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# ... Load base Gemma model using KerasNLP ... |
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# ... Enable LoRA adapters on target layers ... |
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# ... Load adapter weights from this repository ... |
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# ... Use tokenizer, model for generation or other tasks ... |
