huggingface/nanoVLM
NanoVLM
nanoVLM is a training framework and toolkit for small vision-language models. It provides a PyTorch-based environment for training and fine-tuning models to associate image inputs with textual descriptions and generate natural language answers.
The project includes a cloud model versioning tool for saving and loading model weights to centralized repositories to synchronize assets across environments. It also features a dedicated evaluation suite for measuring the accuracy and reliability of vision-language models against standard task datasets.
The framework covers GPU resource planning through VRAM consumption measurement and manages training stability with checkpoint-based state persistence and batch-based memory management.
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.
Star history
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