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

Descubre los mejores repositorios open-source con nuestra búsqueda potenciada por IA.

ExplorarBúsquedas curadasAlternativas open-sourceSoftware autohospedableBlogMapa del sitio
ProyectoServidor MCPAcerca deCómo clasificamosPrensa
Aviso legalPrivacidadTérminos
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 repositorios

Awesome GitHub RepositoriesMulti-Turn Serving Optimizations

Specialized serving techniques for stateful multi-turn interactions, such as session-affinity routing and prefix caching.

Distinct from Optimized Model Serving: Focuses on stateful multi-turn interaction efficiency specifically, whereas Optimized Model Serving is general high-performance serving.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Multi-Turn Serving Optimizations. Refine with filters or upvote what's useful.

Awesome Multi-Turn Serving Optimizations GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • yangjianxin1/fireflyAvatar de yangjianxin1

    yangjianxin1/Firefly

    6,642Ver en GitHub↗

    Firefly is a training framework and inference engine for large language models. It functions as a toolkit for pre-training and fine-tuning various open-weight architectures, providing a system for model alignment and parameter-efficient fine-tuning. The project includes utilities for merging adapter weights back into base models to create standalone files. It also provides a model alignment toolkit to format training data according to specific prompt templates, ensuring conversational consistency across different models. The framework supports distributed model training and preference-based

    Provides optimized serving for stateful multi-turn interactions using memory-efficient inference.

    Pythonalpacaaquilabaichuan
    Ver en GitHub↗6,642
  • thudm/slimeAvatar de THUDM

    THUDM/slime

    4,259Ver en GitHub↗

    SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving. The fra

    Implements session-affinity routing to reuse prefix caches across multi-turn interactions.

    Python
    Ver en GitHub↗4,259
  1. Home
  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Infrastructure
  5. Optimization & Inference
  6. Serving & Runtime
  7. Large Language Model Optimization
  8. Optimized Model Serving
  9. Multi-Turn Serving Optimizations