Explorar
Explora todas las tecnologías del grafo por tipo y categoría.
Por tipo
Por categoría
- Algoritmos 30
- Modelos 16
- Modelos de embedding 8
- Técnicas 35
- Prompting 10
- Frameworks 12
- Frameworks de LLM 6
- Bases de datos 3
- Bases de datos vectoriales 8
- Motores de búsqueda 4
- Infraestructura 15
- MLOps 4
- Evaluación 6
- Hardware 11
- Seguridad y alineación 6
- Aplicaciones 7
- Multimodal 5
- Agentes de IA 4
- Benchmarks 11
- Conjuntos de datos 7
12 technologies shown
- PyTorch framework The dominant deep learning framework, built around dynamic computation graphs.
- TensorFlow framework Google's deep learning framework, oriented toward deployment at scale.
- Sentence Transformers framework Library for embedding sentences and passages into vectors.
- Diffusers library Library of diffusion model implementations and pipelines.
- Keras framework High-level modelling API that runs on several backends.
- DSPy framework Builds model pipelines as programs whose prompts are optimised rather than written.
- DeepSpeed framework Training optimisation library for large models across many GPUs.
- AutoGen framework Framework for multi-agent conversations where agents critique each other.
- JAX framework NumPy-like array library with automatic differentiation and XLA compilation.
- Unsloth framework Fine-tuning implementation with hand-written kernels for speed and low memory.
- CrewAI framework Defines agent teams with roles and tasks, and runs them in order.
- Axolotl framework Configuration-driven fine-tuning runner for many model architectures.