Update README.md

README.md

@@ -13,43 +13,78 @@ tags:
 - finance
 - chemistry
 - biology
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+Certainly! Here's the complete README in Markdown format:
 
-# threatthriver/Gemma-7B-LoRA-Fine-Tuned  
+# 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**]. 
+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 ([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
+- **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:** [Describe your dataset here, including its source, size, and any notable features. If possible, provide a link to the dataset or its description.]
+- **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:
+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.
 
-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.
+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.
 
-**Example Code Structure (Conceptual):**
+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 appropriate tokenizer
+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.
 
-# ... Load base Gemma model using KerasNLP ...
+## Notes
 
-# ... Enable LoRA adapters on target layers ... 
+- 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.
 
-# ... Load adapter weights from this repository ...
+## License
 
-# ... Use tokenizer, model for generation or other tasks ...
+This project is licensed under the [MIT License](LICENSE).