Update README.md

README.md

@@ -1,3 +1,124 @@
----
-license: gemma
----
+---
+license: gemma
+language:
+- en
+- hi
+base_model:
+- google/gemma-2-2b
+pipeline_tag: question-answering
+library_name: keras
+tags:
+- legal
+---
+
+# Gemma Causal Language Model (GemmaCausalLM)
+
+This repository contains the configuration and metadata for the `GemmaCausalLM` model, a powerful causal language model designed for advanced NLP tasks such as text generation, dialogue systems, and autoregressive language modeling.
+
+---
+
+## Model Overview
+
+### **1. Core Architecture**
+The `GemmaCausalLM` combines a robust backbone with an intelligent preprocessor, providing an efficient setup for NLP tasks. Below are its key components:
+
+#### **Backbone (`GemmaBackbone`):**
+- **Vocabulary Size**: 256,000 tokens.
+- **Model Depth**: 26 layers.
+- **Attention Configuration**: 
+  - Query Heads: 8
+  - Key-Value Heads: 4
+  - Head Dimension: 256
+  - Sliding Window Attention: Enabled (window size: 4096).
+- **Dimensions**:
+  - Hidden Dimension: 2,304
+  - Intermediate Dimension: 18,432
+- **Normalization and Regularization**:
+  - Layer Normalization (Epsilon: 1e-6).
+  - Post-feedforward and post-attention normalization enabled.
+- **Soft Caps**:
+  - Final Logit Soft Cap: 30.0
+  - Attention Logit Soft Cap: 50.0
+- **Dropout**: Disabled.
+
+#### **Preprocessor (`GemmaCausalLMPreprocessor`):**
+- **Tokenizer (`GemmaTokenizer`)**:
+  - Configuration File: `tokenizer.json`.
+  - Adds BOS (Beginning of Sequence) and EOS (End of Sequence) tokens.
+- **Sequence Length**: 512.
+- **Data Type**:
+  - Float32 for preprocessor computations.
+  - Int32 for tokenized inputs.
+
+---
+
+## Metadata
+
+- **Keras Version**: `3.5.0`
+- **Keras Hub Version**: `0.17.0`
+- **Parameter Count**: `2,617,270,528` (2.6 billion parameters).
+- **Date Saved**: `2024-11-18@13:59:51`
+
+This metadata ensures reproducibility and provides insights into the complexity of the model.
+
+---
+
+## Applications
+
+This model is designed for tasks requiring causal language modeling, including but not limited to:
+- Text Generation.
+- Dialogue Systems.
+- Autoregressive NLP tasks.
+
+---
+
+## Model Files
+
+- **Backbone Configuration**: 
+  The core architecture details for `GemmaBackbone`.
+- **Preprocessor Configuration**: 
+  Tokenization and sequence preprocessing setup.
+- **Tokenizer File**: 
+  `tokenizer.json`.
+- **Preprocessor File**: 
+  `preprocessor.json`.
+
+---
+
+## Setup and Usage
+
+1. **Dependencies**:
+   Ensure the following libraries are installed:
+   ```bash
+   pip install keras keras_hub
+   ```
+
+2. **Model Loading**:
+   The model can be loaded as follows:
+   ```python
+   from keras_hub.src.models.gemma.gemma_causal_lm import GemmaCausalLM
+
+   model = GemmaCausalLM.from_config(config_file="path/to/config.json")
+   ```
+
+3. **Inference**:
+   Use the preprocessor to tokenize input text and generate predictions with the model.
+
+   ```python
+   preprocessor = model.get_preprocessor()
+   inputs = preprocessor.tokenize("Your input text here.")
+   outputs = model.predict(inputs)
+   print(outputs)
+   ```
+
+---
+
+## Contributions
+
+Feel free to contribute to this repository by improving configurations, extending functionality, or reporting issues.
+
+---
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for details.