huggingface/smol-course
Smol Course
This project is an educational program focused on the alignment of small language models. It provides a technical curriculum and a series of courses designed to teach how to align models with human preferences and behaviors.
The material covers the implementation of preference optimization algorithms and the adaptation of vision-language models to process both text and image data simultaneously. It also includes instructional guides on synthetic data generation to improve model performance in specialized domains.
The curriculum encompasses supervised fine-tuning workflows, the use of chat templates to teach models to follow instructions, and the application of benchmark-driven evaluations to measure model accuracy and reliability.
The course is delivered via Jupyter Notebooks.
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.
Star history
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