awesome-repositories.com
ब्लॉग
MCP
awesome-repositories.com

AI-संचालित खोज के साथ बेहतरीन ओपन-सोर्स रिपॉजिटरी खोजें।

एक्सप्लोर करेंक्यूरेटेड खोजेंओपन-सोर्स विकल्पसेल्फ-होस्टेड सॉफ्टवेयरब्लॉगसाइटमैप
प्रोजेक्टMCP सर्वरहमारे बारे मेंहम रैंकिंग कैसे करते हैंप्रेस
कानूनीगोपनीयताशर्तें
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 रिपॉजिटरी

Awesome GitHub RepositoriesTraining Data Validation Tools

Utilities for computing statistics, schema inference, and anomaly detection in training datasets.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Training Data Validation Tools. Refine with filters or upvote what's useful.

Awesome Training Data Validation Tools GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • tensorflow/tensorflowtensorflow का अवतार

    tensorflow/tensorflow

    195,697GitHub पर देखें↗

    TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr

    Calculates descriptive statistics and detects anomalies within datasets to verify the integrity of training inputs.

    C++deep-learningdeep-neural-networksdistributed
    GitHub पर देखें↗195,697
  • mistralai/mistral-finetunemistralai का अवतार

    mistralai/mistral-finetune

    3,077GitHub पर देखें↗

    This project is a language model finetuning framework designed to adapt large language models to specific datasets using supervised fine-tuning and low-rank adaptation. It serves as a distributed training manager that coordinates workloads and synchronizes gradients across multiple processing units to scale performance. The framework includes a specialized toolkit for low-rank adaptation to update a subset of model weights, reducing memory and hardware requirements. It provides capabilities for instruction fine-tuning, domain adaptation, and the optimization of function calling to improve how

    Provides utilities for checking formatting errors and estimating total training time based on dataset size.

    Python
    GitHub पर देखें↗3,077
  1. Home
  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Frameworks
  5. Training Systems
  6. Training Data Validation Tools