Machine learning

Provides guidance for experiment tracking with SwanLab.

Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM…

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID…

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward…

GGUF format and llama.cpp quantization for efficient CPU/GPU inference.

Half-Quadratic Quantization for LLMs without calibration data.

AWS Startups reference content — Activate FAQ, credits guide, programs, partner offers, sample architectures, and hundreds of learn articles spanning…

LLM observability platform for tracing, evaluation, and monitoring.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime.

Serverless GPU cloud platform for running ML workloads.

Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods.

Open-source AI observability platform for LLM tracing, evaluation, and monitoring.

AWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly…

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework.

High-performance vector similarity search engine for RAG and semantic search.

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.

Distributed training orchestration across clusters.

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.

Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms.

Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).

Optimize content for AI-generated responses and LLM citations (ChatGPT, Perplexity, Google AI Overview, Claude, Gemini).