h2oai/h2o-llmstudio
H2o Llmstudio
h2o-llmstudio est un framework d'entraînement de modèle de langage qui fournit une interface graphique sans code pour le réglage fin (fine-tuning) de grands modèles de langage sur des ensembles de données personnalisés. Il fonctionne comme un outil spécialisé pour gérer le cycle de vie de l'entraînement, de la configuration des hyperparamètres à la surveillance des métriques de performance.
Le projet se distingue par un orchestrateur d'entraînement multi-GPU qui distribue les charges de travail via un traitement parallèle des données et un outil d'adaptation de bas rang pour un réglage fin économe en mémoire. Il inclut également un tableau de bord d'évaluation de modèle doté d'une interface de chat interactive pour vérifier les performances conversationnelles et la qualité des réponses.
La plateforme couvre une large surface de capacités, y compris la préparation des ensembles de données avec mappage de schéma, la quantification de modèle pour réduire l'empreinte mémoire et la gestion des expériences pour comparer les exécutions d'entraînement. Elle fournit également des utilitaires pour l'exportation de modèles locaux et la publication vers des hubs de modèles communautaires.
Le système inclut une interface en ligne de commande pour déclencher des expériences et gérer les fichiers de sortie au sein de flux de travail automatisés.
Features
- Graphical User Interfaces - Provides a no-code graphical interface for configuring hyperparameters and training large language models on custom datasets.
- No-Code Training Interfaces - Provides a no-code graphical interface for configuring hyperparameters and managing LLM training experiments.
- Distributed GPU Training - Distributes large language model training workloads across multiple GPUs using data parallel processing.
- Distributed Training - Distributes LLM training workloads across multiple GPUs to increase processing speed and model capacity.
- Data-Parallel Training - Implements data-parallel training to distribute LLM fine-tuning workloads across multiple GPUs.
- No-Code Fine-Tuning Interfaces - Provides a no-code graphical interface for fine-tuning large language models on custom datasets.
- Language Model Fine-Tuning - Provides a framework for adjusting pre-trained language models using memory-efficient training methods.
- LLM Experiment Management - Tracks multiple training runs and compares hyperparameters to identify the best performing model configuration.
- Training Lifecycle Management - Provides capabilities to launch, stop, rename, or clone training experiment configurations to manage the development lifecycle.
- Interactive Chat Evaluators - Features an interactive chat interface to verify the response quality and accuracy of fine-tuned models.
- Low-Rank Adaptation - Implements low-rank adaptation (LoRA) and PEFT techniques for memory-efficient fine-tuning of foundation models.
- Quantized Fine-Tuning Frameworks - Ships a comprehensive framework that integrates weight quantization and adapter training for efficient model adaptation.
- Model Quantization - Implements quantization techniques to reduce the memory footprint of large language models.
- Quantized Training - Integrates weight precision reduction directly into the training process to lower VRAM requirements.
- Training Dataset Preparation - Provides tools for importing and combining data from various sources to prepare training ground truth.
- Dialogue Dataset Structuring - Transforms raw data columns into structured conversational roles and schemas for language model training.
- Experiment Management - Offers a system to run and compare training experiments and monitor metrics to determine optimal hyperparameters.
- ML Dataset Imports - Enables loading training data from local files, cloud storage, or public hubs into the active environment.
- Conversational Role Mapping - Provides a system for defining training data schemas and mapping dataset columns to conversational roles.
- AI Interaction Sandboxes - Provides an isolated chat-based sandbox for experimenting with and validating fine-tuned model responses.
- Training Progress Monitoring - Ships visual tools including loss charts and system logs to track model training progress in real time.
- Model Evaluation Metrics - Provides visualization and analysis tools for quantitative model performance metrics to compare different experiments.
- Interactive Chat Evaluators - Provides an interactive chat interface to verify the performance and accuracy of trained models through live sessions.
- Training Metric Streaming - Streams training loss and performance telemetry in real-time to external monitoring dashboards.
- Evaluation Dashboards - Includes a workspace for visually tracking quality metrics and comparing validation results across multiple training runs.
- Fine-Tuning Frameworks - Framework and GUI for no-code model fine-tuning.
- Fine-Tuning Frameworks - Framework and GUI for no-code model fine-tuning.
