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3 Repos

Awesome GitHub RepositoriesConversation Structure Validation

Validation of data imports to ensure they adhere to a standardized conversation format.

Distinct from Structural Data Validators: Unlike general structural validation, this specifically targets the conversation schema required for LLM training.

Explore 3 awesome GitHub repositories matching data & databases · Conversation Structure Validation. Refine with filters or upvote what's useful.

Awesome Conversation Structure Validation GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • oumi-ai/oumiAvatar von oumi-ai

    oumi-ai/oumi

    8,858Auf GitHub ansehen↗

    Oumi is a comprehensive large language model development platform designed for synthesizing data, fine-tuning models, and running performance evaluations. It serves as a unified environment for the entire model lifecycle, encompassing a training and fine-tuning suite, an evaluation framework, and tools for synthetic data generation and model distillation. The platform is distinguished by its iterative, failure-driven synthesis approach, which analyzes model weaknesses during evaluation to generate targeted training data. It utilizes an LLM-based judge framework to programmatically score respo

    Converts various file formats into a standardized conversation structure while validating schema compatibility.

    Pythondpoevaluationfine-tuning
    Auf GitHub ansehen↗8,858
  • openai/harmonyAvatar von openai

    openai/harmony

    4,409Auf GitHub ansehen↗

    Harmony ist ein KI-SDK, das für das Tokenisieren von Konversationen, das Formatieren von Reasoning-Layouts, das Parsen von Rohausgaben und das Definieren von Tool-Call-Schemata entwickelt wurde. Es bietet ein System zur Konvertierung strukturierter Dialoge und Tool-Calls in die Token-Sequenzen, die für die Inferenz und das Training von Large Language Models erforderlich sind. Das Projekt enthält einen Output-Formatter, der Reasoning-Ketten und Multi-Channel-Ausgaben in konsistente Layouts strukturiert, um Token-Verlust zu vermeiden. Es bietet zudem einen Response-Parser, der rohe Completion-Token und Live-Streams zurück in strukturierte Nachrichtenobjekte und Rollen transformiert. Das SDK verwaltet die Tool-Integration durch ein Framework zur Definition aufrufbarer Funktionen und Namespaces. Es bietet zudem Funktionen für Echtzeit-Token-Parsing, Konfiguration des Modellverhaltens und zustandsbehaftete Konversations-Serialisierung.

    Organizes messages between users and assistants into formal data structures to facilitate consistent interaction with a model.

    Rust
    Auf GitHub ansehen↗4,409
  • dllxw/baby-llama2-chineseAvatar von DLLXW

    DLLXW/baby-llama2-chinese

    2,891Auf GitHub ansehen↗

    This project is a training pipeline and framework for developing Chinese language models based on the Llama 2 architecture. It functions as a distributed GPU trainer and dataset preprocessing toolkit designed for both the initial pre-training of baseline models and subsequent supervised fine-tuning. The system distinguishes itself through a specialized workflow for Chinese text, incorporating a data curation pipeline that uses similarity hashing for deduplication and a tokenization process that converts raw text into memory-mapped binary files for efficient disk access. It implements a superv

    Filters and cleans raw conversational datasets into structured formats by enforcing prompt and answer length constraints.

    Python
    Auf GitHub ansehen↗2,891
  1. Home
  2. Data & Databases
  3. Structural Data Validators
  4. Conversation Structure Validation

Unter-Tags erkunden

  • Conversational Data StructuresFormal data structures for organizing messages between users and assistants for consistent model interaction. **Distinct from Conversation Structure Validation:** Focuses on the organization and structure of the dialogue messages, not just the validation of the format.