Update README

README.md

@@ -4,4 +4,66 @@ language:
 - en
 base_model:
 - google/gemma-2-2b
----
+---
+# GemmaLM-for-Cannabis
+
+This repository contains a fine-tuned version of the Gemma 2B model, specifically adapted for cannabis-related queries using Low Rank Adaptation (LoRA).
+
+## Model Details
+
+- **Base Model**: Gemma 2B
+- **Fine-tuning Method**: Low Rank Adaptation (LoRA)
+- **LoRA Rank**: 4
+- **Training Data**: Custom dataset derived from cannabis strain information
+- **Task**: Causal Language Modeling for cannabis-related queries
+
+## Fine-tuning Process
+
+The model was fine-tuned using a custom dataset created from cannabis strain information. The dataset includes details about various cannabis strains, their effects, flavors, and descriptions. The fine-tuning process involved:
+
+1. Preprocessing the cannabis dataset into a prompt-response format
+2. Implementing LoRA with a rank of 4 to efficiently adapt the model
+3. Training for a limited number of epochs with a small subset of data for demonstration purposes
+
+## Usage
+
+This model can be used to generate responses to cannabis-related queries. Example usage:
+
+```python
+import keras
+import keras_nlp
+
+# Load the model
+model = keras.models.load_model("gemma_lm_model.keras")
+
+# Set up the sampler
+sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)
+model.compile(sampler=sampler)
+
+# Generate a response
+prompt = "Instruction:\nWhat does OG Kush feel like\nResponse:\n"
+response = model.generate(prompt, max_length=256)
+print(response)
+```
+
+## Limitations
+
+- The model was fine-tuned on a limited dataset for demonstration purposes. For production use, consider training on a larger dataset for more epochs.
+- The current LoRA rank is set to 4, which may limit the model's adaptability. Experimenting with higher ranks could potentially improve performance.
+
+## Future Improvements
+
+To enhance the model's performance, consider:
+
+1. Increasing the size of the fine-tuning dataset
+2. Training for more epochs
+3. Experimenting with higher LoRA rank values
+4. Adjusting hyperparameters such as learning rate and weight decay
+
+## License
+
+Please refer to the Gemma model's original license for usage terms and conditions.
+
+## Acknowledgements
+
+This project uses the Gemma model developed by Google. We acknowledge the Keras and KerasNLP teams for providing the tools and frameworks used in this project.