create-custom-prompt
Prompt for creating custom prompt files
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Prompt for creating custom prompt files
Group Relative Policy Optimization for reinforcement learning from human feedback. Covers GRPOTrainer, reward function design, policy optimization, and KL divergence constraints for stable RLHF training. Includ...
Fast inference with Unsloth and vLLM backend. Covers model loading, fast_generate(), thinking model output parsing, and memory management for efficient inference.
Supervised Fine-Tuning with SFTTrainer and Unsloth. Covers dataset preparation, chat template formatting, training configuration, and Unsloth optimizations for 2x faster instruction tuning. Includes thinking mo...
Open WebUI AI chat interface management via Podman Quadlet. Provides a web UI for interacting with Ollama models. Use when users need to configure, start, or manage the Open WebUI service.
Direct REST API operations for Ollama using the requests library. Covers all /api/* endpoints for model management, text generation, chat completion, embeddings, and streaming responses.
GPU monitoring and performance metrics for Ollama inference. Check GPU status, VRAM usage, loaded models, and inference performance metrics like tokens per second.
Transformer architecture fundamentals. Covers self-attention mechanism, multi-head attention, feed-forward networks, layer normalization, and residual connections. Essential concepts for understanding LLMs.
Retrieval-Augmented Generation (RAG) for grounding LLM responses with external knowledge. Covers document chunking, embeddings, vector stores (pandas, ChromaDB), similarity search, and conversational RAG pipeli...
Import GGUF models from HuggingFace into Ollama. Pull models directly using the hf.co/ prefix, track download progress, and use imported models for inference.
LocalAI local inference API management via Podman Quadlet. Provides an OpenAI-compatible API for local model inference with GPU acceleration. Use when users need to configure, start, or manage the LocalAI servi...
Official ollama Python library for LLM inference. Provides a clean, Pythonic interface for text generation, chat completion, embeddings, model management, and streaming responses.
Aggregate management for all AI pod services. Provides status overview and bulk operations across all pod containers (ollama, jupyter, comfyui, openwebui, localai, fiftyone, jellyfin, runners).
OpenAI compatibility layer for Ollama. Use the official OpenAI Python library to interact with Ollama, enabling easy migration from OpenAI and compatibility with LangChain, LlamaIndex, and other OpenAI-based to...
Ollama LLM inference server management via Podman Quadlet. Single-instance design with GPU acceleration for running local LLMs. Use when users need to configure Ollama, pull models, run inference, or manage the...
ComfyUI node-based Stable Diffusion interface. GPU-accelerated image generation with custom node support and CivitAI model downloads. Use 'ujust comfyui' for configuration, lifecycle management, and model/node...
LangChain framework for LLM applications. Covers model wrappers (HuggingFace, Ollama), prompt templates, few-shot learning, output parsing, and chaining techniques for building sophisticated LLM workflows.
Parameter-efficient fine-tuning with LoRA and Unsloth. Covers LoraConfig, target module selection, QLoRA for 4-bit training, adapter merging, and Unsloth optimizations for 2x faster training.
LLM evaluation and prompt optimization with Evidently.ai. Covers text descriptors, dataset metrics, LLM-as-a-Judge patterns, and automated prompt optimization for classification and generation tasks.
Reward model training for RLHF pipelines. Covers RewardTrainer, preference dataset preparation, sequence classification heads, and reward scaling for stable reinforcement learning. Includes thinking quality sco...
Design Telegram bot keyboards, buttons, and conversational flows with proper UX patterns. Includes inline keyboards, reply keyboards, button callbacks, and progressive disclosure for better mobile UX.
Improve prompts for AI agents and Telegram bots using OpenAI's prompt engineering best practices. Analyzes clarity, specificity, context, and output format. Returns structured improvements.
Expert in designing Telegram bot interfaces with keyboards, buttons, conversational flows, and mobile-first UX patterns.
Create async FastAPI endpoints with proper error handling, dependency injection, service/repository patterns, and type safety. Includes async database queries, pagination, filtering, and Gemini integration.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.