AI & LLM Engineering · lazyFrogLOL/Harness_Engineering

Guidance for implementing neural network inference (like GPT-2) under extreme code size constraints.

lazyFrogLOL/Harness_Engineering

Guidance for implementing batching schedulers for LLM inference systems with compilation-based accelerators.

lazyFrogLOL/Harness_Engineering

This skill provides guidance for implementing PyTorch pipeline parallelism for distributed training of large language models.

lazyFrogLOL/Harness_Engineering

This skill provides guidance for implementing tensor parallelism in PyTorch, specifically column-parallel and row-parallel linear layers.

lazyFrogLOL/Harness_Engineering

This skill provides guidance for training FastText text classification models with constraints on accuracy and model size.

lazyFrogLOL/Harness_Engineering

Guidance for building and training with the Caffe deep learning framework on CIFAR-10 dataset.

lazyFrogLOL/Harness_Engineering

Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks).

lazyFrogLOL/Harness_Engineering