461 skills found · Page 4 of 16
Orchestra-Research / ml-training-recipesBattle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation
Orchestra-Research / chromaOpen-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval.
Orchestra-Research / sentence-transformersFramework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks.
Orchestra-Research / clipOpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning.
Orchestra-Research / whisperOpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing.
Orchestra-Research / peft-fine-tuningParameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods
Orchestra-Research / grpo-rl-trainingExpert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
Orchestra-Research / qdrant-vector-searchHigh-performance vector similarity search engine for RAG and semantic search
Orchestra-Research / audiocraft-audio-generationPyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen)
Orchestra-Research / blip-2-vision-languageVision-language pre-training framework bridging frozen image encoders and LLMs
Orchestra-Research / knowledge-distillationCompress large language models using knowledge distillation from teacher to student models
Orchestra-Research / long-contextExtend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques
Orchestra-Research / model-pruningReduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT
Orchestra-Research / speculative-decodingAccelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques
Orchestra-Research / axolotlExpert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
Orchestra-Research / miles-rl-trainingProvides guidance for enterprise-grade RL training using miles, a production-ready fork of slime
Orchestra-Research / simpo-trainingSimple 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.
Orchestra-Research / torchforge-rl-trainingProvides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms
Orchestra-Research / verl-rl-trainingProvides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL)
Orchestra-Research / awq-quantizationActivation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss
Orchestra-Research / quantizing-models-bitsandbytesQuantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss
Orchestra-Research / optimizing-attention-flashOptimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction
Orchestra-Research / gguf-quantizationGGUF format and llama.cpp quantization for efficient CPU/GPU inference
Orchestra-Research / llama-cppRuns LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
Orchestra-Research / tensorrt-llmOptimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
Orchestra-Research / serving-llms-vllmServes LLMs with high throughput using vLLM's PagedAttention and continuous batching
Orchestra-Research / phoenix-observabilityOpen-source AI observability platform for LLM tracing, evaluation, and monitoring
ConardLi / gpt-image-2面向 GPT Image 2 的图像生成 / 编辑技能。可在 3 种环境下使用：(A) Garden 本地模式，通过 OpenAI 兼容接口直接出图并落盘；(B) Host-Native 模式，把本 Skill 当作提示词工程指引，把渲染好的 prompt 交给宿主 Agent 自带的图像工具出图；(C) Advisor 模式，宿主无任何图像工具时退化为高质量 prompt 顾问。涵盖 18 大类、80+ 个结构化模板，覆盖海报 / UI / 产品 / 信息图 / 学术图 / 技术架构图 / 漫画 / 头像 / 流程板 / 电影分镜 / IP 周边 / 编辑工作流等场景。
Jeffallan / fine-tuning-expertUse when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, Op…
Jeffallan / prompt-engineerWrites, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites
