Orchestra Research
Official@orchestra-research
Offers advanced infrastructure for large-scale model training, distributed inference optimization, and rigorous interpretability research for enterprise-grade neural architectures.
Agent Skills by Orchestra Research
Showing 98 vetted skills indexed across 2 GitHub repositories.
Orchestra-Researchpresenting-conference-talks
Generates Beamer LaTeX and PPTX conference slides with speaker notes from research papers.
Orchestra-Researchara-rigor-reviewer
Reviews Agent-Native Research Artifacts across six epistemic dimensions and writes a severity-ranked level2_report.json.
Orchestra-Researchfine-tuning-serving-openpi
Fine-tune and serve OpenPI pi0 robot policies using JAX or PyTorch backends.
Orchestra-Researchexperiment-tracking-swanlab
Track ML experiments with SwanLab metrics, media logging, and framework integrations.
Orchestra-Researchautoresearch
Orchestrates autonomous AI research projects using a two-loop experiment and synthesis architecture.
Orchestra-Researchacademic-plotting
Generates publication-quality ML paper figures via Gemini diagrams and matplotlib charts.
Orchestra-Researchsystems-paper-writing
Structure systems conference papers with paragraph-level blueprints, venue checklists, and LaTeX templates.
Orchestra-Researchara-research-manager
Records research decisions, experiments, and claims into a provenance-tagged ara/ directory after sessions.
Orchestra-Researchara-compiler
Compiles research inputs into structured Agent-Native Research Artifacts with claims, evidence, and exploration graphs.
Orchestra-Researchevaluating-cosmos-policy
Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa robot simulation benchmarks.
Orchestra-Researchfine-tuning-openvla-oft
Fine-tunes and evaluates OpenVLA-OFT robot policies with LoRA and continuous action heads.
Orchestra-Researchml-training-recipes
Provides PyTorch training recipes covering optimizers, LR scheduling, mixed precision, and debugging across ML domains.
Orchestra-Researchevolving-ai-agents
Evolves and optimizes AI agents through LLM-driven mutation of prompts, skills, and memory against benchmarks.
Orchestra-Researchml-paper-writing
Write publication-ready ML papers with LaTeX templates and verified citations.
Orchestra-Researchserving-llms-vllm
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.
Orchestra-Researchtensorrt-llm
Optimizes LLM inference on NVIDIA GPUs using TensorRT compilation, quantization, and multi-GPU parallelism.
Orchestra-Researchsglang
Serve LLMs with RadixAttention prefix caching and constrained structured generation.
Orchestra-Researchllama-cpp
Runs quantized GGUF LLM inference on CPUs, Apple Silicon, and non-NVIDIA GPUs.
Orchestra-Researchlambda-labs-gpu-cloud
Launch and manage Lambda Labs GPU cloud instances for ML training and inference.
Orchestra-Researchskypilot-multi-cloud-orchestration
Orchestrate machine learning workloads across AWS, GCP, Azure, and Kubernetes.
Orchestra-Researchmodal-serverless-gpu
Deploy and run ML workloads on Modal's serverless GPU cloud platform.
Orchestra-Researchnemo-curator
Curate LLM training datasets with GPU-accelerated deduplication, filtering, and PII redaction.
Orchestra-Researchray-data
Process large ML datasets with distributed streaming execution across CPU and GPU clusters.
Orchestra-Researchdspy
Build declarative LM pipelines with automatic prompt optimization using DSPy.
Frequently Asked Questions About Orchestra Research
FAQPage SchemaWhat specific tasks are enabled by these model training and inference capabilities?▼
These capabilities enable end-to-end model development, including distributed pretraining, fine-tuning via LoRA/QLoRA, and high-throughput inference serving. Users can perform complex tasks like model merging, sparse autoencoder training for interpretability, and large-scale reinforcement learning alignment using GRPO or PPO methods.
Which technical personas benefit most from these research-grade implementations?▼
Machine learning engineers, research scientists, and infrastructure architects focused on deep learning performance benefit most. These resources are designed for practitioners building custom foundation models, optimizing production inference pipelines, or conducting rigorous causal analysis on neural network internals.
What are the primary prerequisites and dependencies for deploying these model architectures?▼
Deployments typically require NVIDIA GPU hardware, PyTorch-compatible environments, and specific distributed backends like Ray or Slurm. Users must manage dependencies such as CUDA, NCCL, and specialized libraries like DeepSpeed, Megatron-Core, or vLLM depending on the specific training or inference objective.