Threatthriver/gemma-7b-lora-instruction-tuned

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

Description

This repository contains LoRA (Low-Rank Adaptation) adapter weights for fine-tuning a Gemma 7B 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
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• 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 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):

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.