explanations for src

Author: astorfi

src/README.md

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+# AI Alignment Project: Workflow and Runnable Example
+
+## 1. Define the Workflow
+The project guides users through the following stages:
+1. **Preprocessing**: Preparing and augmenting the dataset.
+2. **Training**: Fine-tuning or RLHF-based training of the LLM.
+3. **Evaluation**: Assessing model alignment through explainability, bias analysis, and safety tests.
+4. **Deployment**: Running an API to interact with the trained model.
+5. **Feedback Loop**: Incorporating user feedback for iterative improvement.
+
+---
+
+## 2. Workflow Integration of Files
+
+### (a) Data Preprocessing
+- **Files**:
+  - `src/preprocessing/preprocess_data.py`
+  - `src/preprocessing/augmentation.py`
+  - `src/preprocessing/tokenization.py`
+- **Workflow**:
+  1. Start with raw or synthetic data (`data/raw/synthetic_data.csv`).
+  2. Use `preprocess_data.py` to clean and tokenize data.
+  3. Augment data with `augmentation.py` to simulate diverse scenarios.
+  4. **Output**: A cleaned and tokenized dataset ready for training.
+
+### (b) Model Training
+- **Files**:
+  - `src/training/fine_tuning.py`
+  - `src/training/rlhf.py`
+  - `notebooks/02_fine_tuning.ipynb` & `03_rlhf.ipynb`
+- **Workflow**:
+  1. Load the preprocessed data.
+  2. Fine-tune a pretrained LLM with `fine_tuning.py`.
+  3. Optionally, enhance alignment with human feedback via `rlhf.py`.
+  4. Log training results using `mlflow_tracking.py`.
+  5. **Output**: A fine-tuned LLM stored as a model artifact.
+
+### (c) Evaluation
+- **Files**:
+  - `src/evaluation/metrics.py`
+  - `src/evaluation/safety_tests.py`
+  - `src/evaluation/bias_analysis.py`
+  - `notebooks/04_evaluation.ipynb`
+- **Workflow**:
+  1. Evaluate the model's responses for alignment using:
+     - Safety metrics (`safety_tests.py`).
+     - Explainability tools (`metrics.py`).
+     - Bias analysis (`bias_analysis.py`).
+  2. Display performance metrics and insights via `explainability_dashboard.py`.
+
+### (d) Deployment
+- **Files**:
+  - `src/deployment/fastapi_app.py`
+  - `src/deployment/endpoints/predict.py`, `feedback.py`
+  - Docker/Kubernetes configs (`deployment/docker-compose.yml`, `deployment/kubernetes`)
+- **Workflow**:
+  1. Start the FastAPI app to serve the trained model (`fastapi_app.py`).
+  2. Use endpoints:
+     - `/predict`: For inference.
+     - `/feedback`: To capture user feedback.
+  3. Deploy in a containerized environment using Docker or Kubernetes.
+
+### (e) Feedback Loop
+- **Files**:
+  - `app/feedback.py`
+  - `src/reinforcement/multi_objective_rl.py`
+- **Workflow**:
+  1. Capture real-world feedback via `/feedback` API or UI (`app/templates/feedback.html`).
+  2. Retrain the model using `multi_objective_rl.py` to incorporate feedback.
+
+---
+
+## 3. Runnable Example: A Hands-On AI Alignment Experiment
+
+### Step 1: Data Preparation
+Run the preprocessing script:
+```bash
+python src/preprocessing/preprocess_data.py --input data/raw/synthetic_data.csv --output data/processed
+```
+
+### Step 2: Fine-Tuning
+Train the model using the preprocessed data:
+```bash
+python src/training/fine_tuning.py --data_dir data/processed --output_dir models/fine_tuned
+```
+
+#### Explanation:
+- **Input**: 
+  - The script processes data from the `data/processed` directory, which contains cleaned and tokenized datasets.
+  
+- **Model Fine-Tuning**: 
+  - The fine-tuning script applies supervised learning to adjust the weights of a pretrained large language model (LLM).
+  - Hyperparameters such as learning rate, batch size, and number of epochs can be customized in the script or via configuration files.
+  - The fine-tuning process adapts the model to perform alignment-specific tasks (e.g., producing safe, unbiased, and interpretable outputs).
+
+- **Output**: 
+  - A fine-tuned model is saved in the `models/fine_tuned` directory. This model is now better aligned with the desired objectives and can be evaluated for safety, bias, and interpretability.
+
+- **Integration with Experiment Tracking**:
+  - If `mlflow_tracking.py` or a similar tracking tool is used, fine-tuning results (e.g., loss curves, evaluation metrics, and hyperparameters) are logged for reproducibility.
+  - Users can compare different runs, evaluate the impact of hyperparameter changes, and select the best-performing model.
+
+- **Key Learnings**:
+  - Fine-tuning allows a general-purpose LLM to be adapted for specific tasks, making it more relevant for real-world alignment challenges.
+  - Regular evaluation during training ensures that the model maintains alignment with predefined objectives (e.g., minimizing bias or toxicity).
+  - Users gain practical experience with data preparation, model training, and the iterative nature of fine-tuning.
+
+- **Next Steps**:
+  1. Evaluate the fine-tuned model using metrics, safety tests, and bias analysis (Step 3: Evaluate Alignment).
+  2. Deploy the fine-tuned model as an API or in an interactive application (Step 4: Start the API).
+
+#### Common Challenges and Solutions:
+1. **Overfitting**:
+   - Problem: The model may overfit on the fine-tuning dataset, losing its generalization ability.
+   - Solution: 
+     - Use regularization techniques such as dropout.
+     - Implement early stopping during training.
+     - Monitor validation loss and tune the dataset size for diversity.
+
+2. **Insufficient Alignment**:
+   - Problem: The fine-tuned model may still produce misaligned or biased outputs.
+   - Solution:
+     - Incorporate Reinforcement Learning with Human Feedback (RLHF) for further alignment.
+     - Use safety tests and bias analysis to identify problematic outputs and retrain iteratively.
+
+3. **Hyperparameter Tuning**:
+   - Problem: Suboptimal hyperparameter settings may lead to poor performance or inefficiency.
+   - Solution:
+     - Use a hyperparameter tuning framework like Optuna or implement grid/random search.
+     - Explore automated scripts for hyperparameter optimization (`ppo_hyperparameter_tuning.py`).
+
+4. **Scalability Issues**:
+   - Problem: Fine-tuning large LLMs may require significant computational resources.
+   - Solution:
+     - Use distributed training methods (`distributed_rl.py`).
+     - Leverage cloud-based GPUs or TPUs for faster training.
+
+#### Practical Tips:
+- Ensure that the dataset used for fine-tuning aligns with the project's ethical and performance goals.
+- Regularly save checkpoints during training to prevent data loss and allow resuming interrupted runs.
+- Log all experiments systematically for reproducibility and knowledge sharing among team members.
+
+#### Real-World Applications:
+- This step can adapt the LLM for tasks such as:
+  - Generating safe conversational responses in chatbots.
+  - Mitigating bias in summarization or text generation.
+  - Enhancing explainability for AI models in sensitive domains like healthcare or law.
+
+By completing this step, you now have a fine-tuned model that serves as the foundation for subsequent evaluation and deployment in your AI alignment project.
+
+