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3 repository-uri

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

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • openbmb/voxcpmAvatar OpenBMB

    OpenBMB/VoxCPM

    29,985Vezi pe 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
    Vezi pe GitHub↗29,985
  • nvidia/tensorrt-llmAvatar NVIDIA

    NVIDIA/TensorRT-LLM

    12,913Vezi pe 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
    Vezi pe GitHub↗12,913
  • zhaochenyang20/awesome-ml-sys-tutorialAvatar zhaochenyang20

    zhaochenyang20/Awesome-ML-SYS-Tutorial

    5,371Vezi pe 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
    Vezi pe GitHub↗5,371
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  3. Decoder Architectures
  4. Custom Decoding Strategies

Explorează sub-etichetele

  • 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.