updates

Author: AseelOmer

google_gemma/README.md

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+# Project: “Green AI Automated RAG Testing with Gemma”
+
+## Overview
+
+We built an interactive retrieval-augmented generation (RAG) pipeline using the
+open-weight model *Gemma 2-2b* by Google and applied it to a standardized text
+and prompt set derived from the Apollo 11 lunar landing.
+
+**The goal:**
+
+* evaluate summarisation, reasoning, retrieval, paraphrasing, and creative tasks
+in a controlled, reproducible way — logging both answer quality and local
+sustainability metrics (energy/carbon emissions) via CodeCarbon.
+
+## Model
+
+**Model ID:** `google/gemma-2-2b-it` (Hugging Face)
+
+**Key attributes:**
+
+* Open-weight decoder-only model trained by Google
+* Supports text-generation and conversational usage
+* Suitable for research, summarisation, reasoning, and retrieval tasks
+* Lightweight enough for deployment on modest compute resources
+
+Model link: [https://huggingface.co/google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it)
+
+## What We Did
+
+1. Created a source document (`source.txt`) using ~1,400 words of selected
+   Wikipedia excerpts on Apollo 11.
+2. Defined a set of 21 standardised prompts spanning five categories:
+   summarisation, reasoning, RAG (fact retrieval), paraphrasing, and creative
+   generation.
+3. Built a document retrieval component using sentence-transformers to chunk
+   the document and select top-k relevant chunks per query.
+4. Developed an interactive notebook workflow that:
+
+   * Accepts a question at runtime
+   * Runs RAG → Draft → Critic → Refiner cycles using Gemma
+   * Tracks local CPU/GPU energy usage and CO₂ emissions with CodeCarbon
+   * Logs each question, answer, timestamp, and emissions to a single
+     append-only log file
+5. Logged runtime latency and emissions per query for performance and
+   sustainability insights.
+
+## How to Use
+
+1. Clone the repository:
+
+   ```bash
+   git clone <YOUR_REPO_URL>
+   cd your_repo_folder
+   ```
+
+2. Place your `source.txt` into `./data/`.
+
+3. Add your Hugging Face API key in the config cell.
+
+4. Run the notebook setup cells, then use the interactive prompt cell to ask
+   questions.
+
+## Why This Matters
+
+* **Reproducibility** — fixed source text and prompt set allow consistent
+  evaluation across models.
+* **Efficiency vs. Accuracy** — emissions are logged alongside outputs to
+  explore trade-offs between model performance and energy cost.
+* **Accessibility** — uses an open model and standard Python tools, making
+  research on small language models feasible even on laptops.