Machine learning

Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware.

Fast structured generation and serving for LLMs with RadixAttention prefix caching.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency.

Builds generative AI applications on Amazon Bedrock.

Startup-tailored AWS architecture advice for non-agent workloads, adjusted to the company's stage (pre-revenue through Series B+), team size, runway, and…

Selects, deploys, and customizes AI models on Amazon SageMaker.

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

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

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).

调研合作伙伴，生成 LLM 生态合作所需的 GTM 和合作方案

获取 Hugging Face 上的模型、数据集和 Space 的统计信息

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.

Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm.

Query LLMs from the CLI — pipe text for summarization, chat interactively, use local or cloud models with llm or aichat.

Author a new ShapeShifter graph-transformation pass for AMD Quark (ONNX or PyTorch) so it conforms to the pass framework's conventions and auto-registers.

Install or verify the AMD Quark package and its dependencies.

Prepare export and downstream evaluation handoff for a planned or completed Quark PTQ run.

Install or verify the correct PyTorch build for a user's accelerator backend before Quark installation.

End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — container setup, vLLM/SGLang/ATOM serving, lm-eval / lighteval / evalscope benchmarks.

End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — container setup, vLLM/SGLang/ATOM serving, lm-eval / lighteval / evalscope benchmarks.

L3 recipe that runs a Torch LLM PTQ end-to-end for AMD Quark — for PyTorch / HuggingFace transformers models (safetensors input): quantize → validate →…

Build a Quark Torch LLM PTQ quantization plan from model analysis and user intent.