Browse skills

Designs and optimizes LangChain application architectures, including agents, chains, and memory structures, for improved performance.

Configures LangChain4J's vector stores for optimal performance and memory management, enhancing retrieval speed & efficiency.

PyTorch FSDP accelerates large model training by intelligently distributing data across multiple GPUs, boosting efficiency and reducing memory footprint.

Deepspeed optimizes large model training by enabling efficient memory usage and faster execution, boosting productivity for AI developers.

This skill optimizes large language model performance by reducing model precision (bitsandbytes) for faster inference and lower memory usage.

This skill efficiently reduces model sizes using BitsAndBytes quantization, enabling faster inference and lower memory requirements – crucial for deployment.

Llama Cpp translates Python code into optimized C++ for faster execution and reduced memory usage of Llama models.

This skill optimizes large language model performance by reducing model precision (bitsandbytes quantization), enabling faster inference and lower memory usage.

Leverages OpenAI's models to store, retrieve, and utilize conversational context for enhanced agent reasoning.

Build custom LangChain agents with pre-configured tools and memory for complex task automation.

Semantic memory search using Qdrant + Ollama embeddings (QRAS). Use as primary method for recalling workspace files, notes, decisions, and prior work before falling back to built-in search.

Optimizes attention flash mechanisms for models like GPT to reduce memory usage and accelerate inference speed, boosting efficiency.