AI Engineering

Implement and deploy the RWKV architecture, a hybrid RNN-Transformer with linear-time inference, infinite context, and no KV cache. Ideal for long-context processing, streaming generation, and memory-constrained deployment.

SentencePiece is a language-independent tokenizer for multilingual models and CJK languages. It supports BPE and Unigram algorithms, with fast processing (50k sentences/sec) and deterministic vocabulary.

Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) with SAELens to decompose neural network activations into interpretable features. Use for discovering features, analyzing superposition, or studying monosemantic representations.

Provides guidance for implementing agentic reinforcement learning (RL) using Meta's torchforge library. Use when you need clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

Provides expert guidance for fine-tuning large language models with Unsloth, achieving 2-5x faster training and 50-80% less memory using LoRA/QLoRA optimization. Use when implementing, debugging, or learning best practices for Unsloth.

Verl RL Training provides guidance for training LLMs with reinforcement learning using verl. Use it when implementing RLHF, GRPO, PPO, or other post-training algorithms at scale with flexible backends.

Investigate classifies one EmDash issue, chooses a repro path, and records reproduction, diagnosis, verification, and any fix with evidence. It is used when you need an end-to-end issue investigation with transcripts behind every verdict.

Prompt Master generates optimized prompts for AI tools. Use it when you need to write, fix, improve, or adapt a prompt for a specific AI tool, including LLMs, coding agents, image/video models, and automation tools.

Django Expert builds Django web applications and REST APIs with Django REST Framework, optimizing ORM queries and configuring JWT authentication. Use it when working with models, serializers, views, or authentication in Django projects.