Browse skills

This skill simplifies running and managing LLMs like VLLM, enabling efficient experimentation and deployment for developers and researchers.

This skill assesses LLM performance within a harness, providing insights into accuracy and efficiency for optimized deployments.

This skill streamlines LLM integration by automatically deploying and configuring GPT models for your projects, boosting AI capabilities quickly.

Provides deployment guidance and assistance for software development workflows.

Serves LLMs like VLLM efficiently for experimentation and deployment, streamlining development workflows and scaling AI applications.

This skill efficiently reduces model sizes using BitsAndBytes quantization, enabling faster inference and lower memory requirements – crucial for deployment.

Helps with data modeling, deployment as part of automating DevOps pipelines and CI/CD workflows workflows.

Llama Cpp converts Llama model prompts into optimized C++ code for faster inference and deployment in various applications.

This skill streamlines LLM integration by automatically deploying and configuring GPT models for your projects, boosting AI capabilities quickly.

This skill deploys and integrates LLMs like ChatGPT for enhanced conversational AI applications and content generation.

This skill assesses LLM performance within a harness environment, providing insights for optimization and ensuring consistent results across deployments.

Helps with LLMs, deployment as part of software development workflows workflows.

This skill automates ML model deployment to various platforms, streamlining workflows and accelerating time-to-value for your AI solutions.

Helps with OpenAI, deployment as part of building AI and machine learning applications workflows.

Automates PyTorch model deployment to cloud environments, streamlining inference serving and scaling workflows.

The Guardrails Reviewer analyzes your AI model’s outputs against predefined guardrails, ensuring safety and compliance – crucial for responsible AI deployment.

Quickly deploy vLLM inference endpoints with a single command, simplifying large language model serving.

Automates vLLM model deployment and scaling across infrastructure for optimized performance and resource utilization.

Helps with Azure, AI, OpenAI as part of deploying and managing cloud infrastructure workflows.

Analyzes and optimizes performance metrics from The Bearded Bears' AS (Automated Systems) deployments for improved efficiency.

This skill automatically deploys your Hugging Face Spaces to production, simplifying deployment and making your models accessible online.

Reduces LLM file sizes & speeds up inference by quantizing GGuf models – making them more efficient to run and deploy.

Nnsight Remote Interpretability analyzes model behavior in real-time across deployed environments, revealing insights for debugging and trust.

GPTQ enables faster, smaller language model inference by quantizing weights – crucial for deploying large models on limited hardware.