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2 dépôts

Awesome GitHub RepositoriesDistribution-Based Sampling

Selection of tokens based on the model's output probability distribution for creative generation.

Distinct from Deterministic Token Sampling Kernels: Focuses on the probabilistic sampling process for text generation, rather than the low-level deterministic GPU kernels.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Distribution-Based Sampling. Refine with filters or upvote what's useful.

Awesome Distribution-Based Sampling GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • mistralai/mistral-srcAvatar de mistralai

    mistralai/mistral-src

    10,821Voir sur GitHub↗

    This project is a large language model inference library and framework designed to run models for text generation, problem solving, and coding assistance. It includes a multimodal framework for processing combined image and text inputs and a tool-use implementation that enables the execution of external functions based on model reasoning. The system features a distributed GPU inference engine that spreads large model workloads across multiple graphics processors to increase processing speed and meet memory requirements. It also provides containerized model deployment through pre-packaged imag

    Selects tokens from a probability distribution using temperature and top-p filtering.

    Jupyter Notebook
    Voir sur GitHub↗10,821
  • yandex/yalm-100bAvatar de yandex

    yandex/YaLM-100B

    3,759Voir sur GitHub↗

    YaLM-100B is a large language model and open-weights AI model designed for generating and processing natural language text. It functions as a multilingual text generator optimized for producing and understanding human language content specifically in English and Russian. The model is built for large scale language modeling and open source AI research, providing a foundation for text-based machine learning tasks. It utilizes a decoder-only transformer architecture with a multilingual embedding space to map English and Russian text into a shared vector space. Its broader capabilities cover nat

    Employs probability distribution-based sampling and greedy selection to determine the final output tokens.

    Python
    Voir sur GitHub↗3,759
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