Provides tools for optimizing, fine-tuning, and deploying large language models with Unsloth, enabling 2x faster training with 80% less memory through model loading, fine-tuning, text generation, and model export capabilities.
Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.
Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.
Transforms AI assistants into a full ML engineering environment for training and fine-tuning models across multiple backends (local GPU, Mistral, Together AI, OpenAI) and cloud providers (Lambda Labs, RunPod, SSH-accessible VPS), with dataset management, experiment tracking, cost estimation, and deployment to Ollama/Open WebUI.
Make any LLM a codebase expert instantly. Provides deep code intelligence through semantic search, architecture mapping, security analysis, and smart context that fits perfectly in token windows.
Persistent memory layer for AI agents with entity resolution, PII detection, AES-256-GCM encryption at rest, and hybrid search. Self-hosted. 100% on LoCoMo benchmark.
