Principalele funcționalități ale josstorer/rwkv-runner sunt: Local Model Runners, Local LLM Tools, OpenAI-Compatible, Chat Interfaces, State-Based Inference Engines, Gated Linear Recurrent Layers, Model Launchers, OpenAI-Compatible Servers.
Alternativele open-source pentru josstorer/rwkv-runner includ: lostruins/koboldcpp — KoboldCPP is a local large language model inference engine and GGUF model runner designed to execute quantized models… ericlbuehler/mistral.rs — mistral.rs is an inference engine for large language models that runs locally and exposes models behind OpenAI and… abetlen/llama-cpp-python — llama-cpp-python provides a Python interface for the llama.cpp library, enabling the execution of large language… meta-pytorch/torchtune — Torchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a… coaidev/coai — CoAI is an enterprise-grade, self-hostable AI gateway platform that unifies access to over 200 AI models from more… antirez/ds4 — ds4 is a local inference engine for DeepSeek models that includes a distributed runtime for splitting transformer…
KoboldCPP is a local large language model inference engine and GGUF model runner designed to execute quantized models on personal hardware. It functions as a multimodal AI server and API gateway, providing OpenAI-compatible endpoints that allow third-party clients to interact with locally hosted models. The project distinguishes itself as an AI storytelling backend, featuring dedicated tools for long-form narrative management through persistent memory, world lore tracking, and character state management. It further extends its capabilities as a multimodal server capable of processing text, im
mistral.rs is an inference engine for large language models that runs locally and exposes models behind OpenAI and Anthropic-compatible APIs. It serves as a multi-model serving platform, capable of loading several models in a single server process with per-request routing and on-demand loading and unloading. The engine supports multimodal inference, processing text alongside images, video, audio, and speech inputs, and includes a quantized model deployment runtime that reduces memory use and speeds up inference on consumer hardware. The project distinguishes itself through an agentic tool exe
llama-cpp-python provides a Python interface for the llama.cpp library, enabling the execution of large language models with hardware acceleration. It functions as a GGUF model loader and a structured text generator capable of running inference servers and multimodal runtimes for processing both text and image inputs. The project distinguishes itself through a local inference server that exposes model capabilities via an OpenAI-compatible web API. It supports advanced execution techniques including speculative decoding, weight quantization, and layer-based GPU offloading to manage memory acro
Torchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a config-driven system for instantiating components, orchestrating distributed training, and managing parameter-efficient fine-tuning with quantization support, all through YAML-based configurations and command-line overrides. The library distinguishes itself through its comprehensive post-training workflow orchestration, combining supervised fine-tuning, preference optimization (DPO, PPO, GRPO), knowledge distillation, and quantization-aware training in a single configurable pip