huggingface/smol-course

Features

• Alignment Techniques - Provides a comprehensive technical curriculum for aligning small language models with human preferences.
• Model Fine-Tuning Guides - Provides a comprehensive educational curriculum and guides for fine-tuning small language models using supervised techniques and chat templates.
• Preference Alignment - Implements preference optimization algorithms to ensure model outputs match desired human behaviors.
• Language Model Fine-Tuning - Utilizes supervised fine-tuning and chat templates to teach models how to follow specific instructions.
• Human Preference Alignment - Covers fine-tuning methods that utilize human feedback to align model outputs with specific values and styles.
• Preference Optimization - Provides a curriculum on implementing preference optimization algorithms to align model outputs with human values.
• Supervised Fine-Tuning - Details supervised fine-tuning workflows using curated instruction and response pairs to adapt pre-trained models.
• Alignment Workflows - Adapts small language models to follow specific instructions and human preferences through targeted fine-tuning.
• LLM Alignment Courses - Provides a structured learning program focused on aligning small language models with human preferences.
• Synthetic Dataset Generators - Teaches the use of automated tools to generate synthetic training data for fine-tuning language and vision models.
• Generation Tutorials - Includes instructional guides on creating artificial training datasets to enhance model performance in specialized domains.
• Chat Template Management - Provides instructional material on defining and formatting structured templates to ensure consistent conversational behavior.
• Multimodal Vision Models - Configures multimodal vision models to interpret visual inputs alongside text for complex tasks.
• Model Performance Evaluators - Provides methods for quantifying model accuracy and reliability by comparing outputs against ground truth labels.
• Multimodal Models - Guides the configuration of vision-language models to process text and images within a shared representation space.
• Multimodal Input Tuples - Teaches how to format mixed-media inputs into tuples required for simultaneous text and image processing.
• Synthetic Dataset Generation - Includes instructional guides on generating synthetic data to improve model performance in specialized domains.
• Preference Optimization Courses - Provides an educational resource for implementing algorithms that ensure model outputs match human-defined values.
• Multimodal Adaptation Guides - Offers instructional material for configuring vision-language models to process text and image data simultaneously.
• Model Evaluation Benchmarks - Implements benchmark-driven evaluations to measure model accuracy and reliability against ground-truth reference data.
• LLM Evaluation - Measures the quality and reliability of model outputs using automated judges and custom metrics.

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