feat: Add "Auto-Train(ing) on HuggingFace" to theory section

src/_quarto.yml

@@ -110,6 +110,8 @@ website:
             text: "Chain of toughts"
           - href: llms/finetuning.qmd
             text: "Fine-tuning and Lora"
+          - href: llms/autotrain.qmd
+            text: "Auto-Train(ing) on HuggingFace"
           - href: llms/rag_systems.qmd
             text: "Retrival Augmented Generation"
 

src/llms/autotrain.qmd

@@ -0,0 +1,48 @@
+# Training Capabilities of AutoTrain: An Exhaustive Guide
+
+[AutoTrain documentation](https://huggingface.co/docs/autotrain/).
+
+## Introduction
+AutoTrain by Hugging Face provides an accessible and efficient way to fine-tune Large Language Models (LLMs). This platform allows users to leverage advanced machine learning techniques with minimal coding. It supports various training methods, including Direct Preference Optimization (DPO), Optimized Reward-based Preference Optimization (ORPO), and Reward training, making it suitable for a wide range of applications.
+
+## Trainers
+
+### Direct Preference Optimization (DPO) Trainer
+The DPO Trainer is designed for scenarios where learning from preferences is essential. Here are its key aspects:
+
+- **Reference Model**: The trainer uses a pre-existing model as a reference to guide the learning process.
+- **Beta Parameter**: This hyperparameter controls the influence of the reference model during training. A higher beta makes the model adhere closely to the reference, while a lower beta allows more deviation.
+- **Prompt and Completion Lengths**: Users must specify the maximum lengths for prompts and completions, ensuring that the model handles input and output sequences appropriately.
+
+### Optimized Reward-based Preference Optimization (ORPO) Trainer
+The ORPO Trainer extends the principles of preference optimization to reward-based scenarios, particularly suited for encoder-decoder models. It includes:
+
+- **Reward Functions**: These are critical for guiding the model based on specific task rewards.
+- **Preference Learning**: Similar to DPO, ORPO involves learning from preferences but is fine-tuned for tasks where rewards are a primary concern.
+- **Prompt and Completion Lengths**: As with DPO, defining these lengths is necessary to manage the input and output sequences.
+
+### Reward Trainer
+The Reward Trainer focuses on reinforcement learning techniques to optimize models based on reward signals. This is particularly useful for tasks where feedback is provided in the form of rewards rather than explicit preferences. Key features include:
+
+- **Reinforcement Learning**: The model learns to maximize rewards through interactions with the environment or dataset.
+- **Reward Signal Optimization**: The trainer is designed to adjust the model’s parameters to enhance the reward signals it receives, making it suitable for complex tasks requiring nuanced optimization.
+
+## Practical Applications
+AutoTrain’s diverse training capabilities make it versatile for various applications:
+
+- **Chatbots and Conversational Agents**: Fine-tuning models to respond accurately based on user preferences or rewards.
+- **Content Generation**: Enhancing models to generate high-quality, relevant content by optimizing for specific rewards.
+- **Sentiment Analysis and Classification**: Training models to classify text based on user-defined preferences or reward functions.
+
+## Setting Up Training
+To set up training using AutoTrain, users need to follow these steps:
+
+1. **Dataset Preparation**: Ensure that the dataset is formatted correctly, with appropriate labels for preference or reward-based tasks.
+2. **Choosing a Trainer**: Select the suitable trainer (DPO, ORPO, or Reward) based on the task requirements.
+3. **Configuring Parameters**: Set the necessary parameters, such as prompt and completion lengths, beta value for DPO, and reward functions for ORPO and Reward trainers.
+4. **Training and Evaluation**: Run the training process and evaluate the model’s performance using validation datasets.
+
+#### Conclusion
+AutoTrain by Hugging Face offers robust tools for fine-tuning LLMs, catering to various needs through its DPO, ORPO, and Reward trainers. These capabilities empower users to create models that not only perform well but also align with specific preferences and reward structures, enhancing the overall effectiveness and applicability of the models.
+
+For more detailed information, you can refer to the [AutoTrain documentation](https://huggingface.co/docs/autotrain/).