awesome-repositories.com
Blog
MCP
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetServeur MCPÀ proposNotre méthodologiePresse
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

3 dépôts

Awesome GitHub RepositoriesCustom Decoding Strategies

Sampling and decoding logic implemented directly within the inference execution pipeline.

Distinct from Decoder Architectures: Focuses on custom sampling logic integrated into the execution pipeline, distinct from general decoder architectures.

Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Custom Decoding Strategies. Refine with filters or upvote what's useful.

Awesome Custom Decoding Strategies GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • openbmb/voxcpmAvatar de OpenBMB

    OpenBMB/VoxCPM

    29,985Voir sur GitHub↗

    VoxCPM is a multilingual speech synthesis system and text-to-speech inference server. It functions as an AI voice cloning tool and a synthetic voice designer, capable of generating natural speech across global languages and regional dialects using a GPU-accelerated audio generator. The project features a speech model fine-tuning framework that supports both full parameter updates and low-rank adaptation for customizing voice characteristics. It enables high-fidelity voice cloning from reference audio, including cross-lingual voice transfer and acoustic environment mimicry, as well as the crea

    Produces high-resolution studio audio by conditioning the output decoder on specific target sample rates.

    Pythonaudiodeeplearningminicpm
    Voir sur GitHub↗29,985
  • nvidia/tensorrt-llmAvatar de NVIDIA

    NVIDIA/TensorRT-LLM

    12,913Voir sur GitHub↗

    TensorRT-LLM is a platform and toolkit designed for compiling, optimizing, and serving transformer-based models on accelerated hardware. It functions as a framework that transforms machine learning models into efficient execution graphs, providing an engine to refine these models for specific hardware to maximize throughput and minimize latency during text generation. The project distinguishes itself through advanced execution strategies that manage the entire inference pipeline. It utilizes kernel-level fusion and static graph execution to optimize mathematical operations and computational f

    Implements custom sampling strategies directly within the execution pipeline to minimize data transfer overhead.

    Pythonblackwellcudallm-serving
    Voir sur GitHub↗12,913
  • zhaochenyang20/awesome-ml-sys-tutorialAvatar de zhaochenyang20

    zhaochenyang20/Awesome-ML-SYS-Tutorial

    5,371Voir sur GitHub↗

    This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr

    Customizes diffusion decoding behavior through external configuration files to decouple algorithm parameters from core inference logic.

    Python
    Voir sur GitHub↗5,371
  1. Home
  2. Artificial Intelligence & ML
  3. Decoder Architectures
  4. Custom Decoding Strategies

Explorer les sous-tags

  • Sample-Rate Conditioned DecodingDecoding strategies that condition the audio output on specific target sample rates to achieve studio-quality resolution. **Distinct from Custom Decoding Strategies:** Distinct from Custom Decoding Strategies: specifically focuses on sample-rate conditioning for audio fidelity.