README.md

---
library_name: transformers
pipeline_tag: text-generation
license: mit
datasets:
- databricks/databricks-dolly-15k
language:
- en
- hi
tags:
- chemistry
- biology
- legal
- art
- code
- finance
- merge
- text-generation-inference
- music
- climate
- medical
---
# 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.

**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 ([Low-Rank Adaptation](https://arxiv.org/abs/2106.09685))
- **LoRA Rank:** 8
- **Dataset:** 
- **Training Framework:** KerasNLP

## How to Use

This 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. Make sure you have the KerasNLP library installed and properly configured.

2. **Enable LoRA:**
   Utilize KerasNLP’s LoRA functionality to enable adapters on the appropriate layers of the Gemma model. Refer to the [KerasNLP LoRA documentation](https://keras.io/guides/transformers/#low-rank-adaptation-lora) for implementation details.

3. **Load Adapter Weights:**
   Load the `adapter_model.bin` and other relevant files from this repository. The `config.json` file provides essential configurations for applying the LoRA adapter weights.

4. **Integration:**
   Integrate this custom loading process into your Hugging Face Transformers-based code. Ensure you handle the merging of adapter weights with the base model appropriately.

## Example Code Structure (Conceptual):

```python
import keras_nlp
from transformers import GemmaTokenizerFast  # Or the appropriate tokenizer from KerasNLP

# Load the base Gemma model using KerasNLP
base_model = keras_nlp.models.Gemma.from_pretrained('google/gemma2_9b_en')

# Enable LoRA adapters on target layers
# Assuming you have a function to enable LoRA, e.g., enable_lora(model, rank)
enable_lora(base_model, rank=8)

# Load adapter weights from this repository
# Assuming you have a function to load the weights, e.g., load_lora_weights(model, weights_path)
adapter_weights_path = 'path_to_your_adapter_weights/adapter_model.bin'
load_lora_weights(base_model, adapter_weights_path)

# Initialize tokenizer
tokenizer = GemmaTokenizerFast.from_pretrained('google/gemma2_9b_en')

# Use the tokenizer and model for generation or other tasks
inputs = tokenizer("Your input text", return_tensors="pt")
outputs = base_model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Requirements

- **KerasNLP:** Install using `pip install keras-nlp`
- **Transformers:** Install using `pip install transformers`
- **Other Dependencies:** Ensure all dependencies required for KerasNLP and Hugging Face Transformers are installed.

## Notes

- Ensure you have the correct versions of KerasNLP and Transformers compatible with each other.
- Custom code for loading and applying LoRA adapters may require adjustments based on your specific use case and the versions of libraries used.

## License

This project is licensed under the [MIT License](LICENSE).