google-deepmind/gemma
Gemma
Gemma ist eine Familie von Open-Weights Large Language Models, die auf einer Decoder-only-Transformer-Architektur basieren. Diese Modelle sind für Textgenerierung und multimodale Konversationen konzipiert und in der Lage, Antworten basierend auf sowohl textuellen als auch visuellen Eingabesequenzen zu verarbeiten und zu generieren.
Das Projekt bietet ein feinabgestimmtes KI-Modell, das Gewichtsanpassungen und Low-Rank-Adaption unterstützt, um die Leistung für bestimmte Aufgaben zu spezialisieren. Es beinhaltet Unterstützung für quantisierte Gewichte, um den Speicherverbrauch zu reduzieren und die Inferenzgeschwindigkeit auf begrenzter Hardware zu erhöhen.
Die Funktionspalette deckt multimodale KI-Integration, Speicheroptimierung durch Parameter-Sharding sowie die Integration externer Tools und APIs zum Abrufen von Echtzeitdaten ab. Es ermöglicht zudem die Generierung von Bildern aus Text und das Sampling strukturierter Textausgaben.
Features
- Open Multimodal Model Deployers - Deploys a family of open and efficient multimodal models capable of text and image generation.
- Open-Weights Models - Provides a family of large language models with publicly available weights for local or cloud execution.
- Decoder Architectures - Implements a transformer-based architecture utilizing causal attention mechanisms for autoregressive sequence generation.
- LLM Fine-Tuning - Fine-tunes large language models on custom datasets using parameter-efficient methods like LoRA.
- GPU Memory Optimizers - Optimizes VRAM usage for large models through quantization and parameter sharding to fit on limited GPUs.
- Multi-Modal Inference Engines - Provides an inference engine capable of processing and analyzing combined text and vision inputs within a single model.
- Multi-modal Language Models - Provides a model capable of processing and generating responses from both textual and visual input sequences.
- Cross-Attention Mechanisms - Integrates visual and textual data by mapping different input modalities into a shared latent space for joint processing.
- Model Deployments - Provides open weights that can be downloaded and deployed on local or cloud hardware.
- Decoder-Only Architectures - Utilizes a decoder-only transformer architecture for autoregressive sequence generation.
- Fine-tunable Models - Supports weight adjustment and low-rank adaptation to specialize performance for particular tasks.
- Multi-Modal AI - Processes and generates responses based on both textual and visual input sequences within a single workflow.
- External Tool Integrations - Connects AI assistants to external utilities and APIs to perform actions and retrieve real-time data.
- Text-to-Image Generators - Implements pipelines that generate high-resolution images from natural language text prompts.
- Language Model Fine-Tuning - Provides frameworks and utilities for adjusting pre-trained language models using memory-efficient training methods.
- Model Fine-Tuning - Provides procedures for adapting pre-trained models to specific datasets or tasks.
- Low-Rank Adaptation - Supports training a small number of additional weight matrices while keeping base weights frozen to reduce hardware requirements.
- Quantized Models - Includes support for quantized weights to reduce memory usage and increase inference speed on limited hardware.
- Weight Quantization - Compresses model weights into lower-precision formats to reduce memory footprint and accelerate inference.
- Tool Use And Integration - Enables the model to call external functions and APIs to retrieve real-time data and perform actions.
- Distributed Parameter Sharding - Partitions large-scale model tensors across multiple compute nodes to facilitate parallel processing and overcome memory limits.
- Deep Learning Frameworks - Open-weights large language models based on advanced research.
