huggingface/nanoVLM
NanoVLM
nanoVLM est un framework d'entraînement et une boîte à outils pour les petits modèles vision-langage. Il fournit un environnement basé sur PyTorch pour entraîner et affiner des modèles afin d'associer des entrées d'image à des descriptions textuelles et de générer des réponses en langage naturel.
Le projet inclut un outil de versioning de modèles dans le cloud pour enregistrer et charger les poids des modèles vers des dépôts centralisés afin de synchroniser les ressources entre les environnements. Il dispose également d'une suite d'évaluation dédiée pour mesurer la précision et la fiabilité des modèles vision-langage par rapport à des jeux de données de tâches standard.
Le framework couvre la planification des ressources GPU via la mesure de la consommation VRAM et gère la stabilité de l'entraînement avec la persistance d'état basée sur des points de contrôle et la gestion de la mémoire par lots.
Features
- Training Frameworks - Provides a comprehensive framework for training and fine-tuning small vision-language models.
- Vision-Language Training - Implements specialized training workflows for models that process both visual and textual data.
- PyTorch Training Frameworks - Provides a high-level structure and utility for executing the training of PyTorch-based models.
- PyTorch Vision Model Trainers - Provides a PyTorch-based environment for optimizing weights and measuring GPU memory during training.
- Vision-Language Inference - Processes combined image and text inputs to generate analytical natural language text.
- Visual-Language Multimodal Integration - Integrates visual feature extractors with language models in a shared embedding space for multimodal processing.
- Visual Question Answering - Generates natural language answers and descriptive captions based on visual content.
- Small VLM Fine-Tuning - Provides streamlined scripts for training and adapting small vision-language models to specific datasets.
- Model Performance Evaluators - Quantifies model accuracy and reliability by comparing predictions against ground truth labels.
- Training Checkpoint Persistence - Provides persistence for model weights and trainer states to ensure fault tolerance and training recovery.
- VRAM Planning Tools - Calculates peak VRAM usage for different model configurations to determine required training hardware.
- Multi-Dataset Vision-Language Evaluators - Evaluates pretrained models across multiple vision-and-language datasets to report task accuracy.
- Model Asset Versioning - Interfaces with remote repositories to store and retrieve model weights for consistent multi-environment deployment.
- Model Version Synchronizers - Synchronizes AI model weights across different development environments via cloud repositories.
- Model Asset Synchronizers - Synchronizes model weights and vocabulary files from remote repositories to local environments.
- VRAM Usage Analysis - Calculates memory requirements for tensors and activations to optimize GPU batch sizes.
- GPU Block Memory Management - Implements GPU memory management to balance data throughput with available video memory.
- Vision-Language Model Benchmarking - Evaluates the accuracy and reasoning of vision-language models against standard benchmark datasets.
- Evaluation Pipelines - Runs models against standardized benchmarks to measure quantitative performance metrics.
- Small Multimodal Models - Small-scale vision-language model.
- Small Vision Models - Compact vision-language model for efficient deployment.
