llmebench
A Flexible Framework for Accelerating LLMs Benchmarking
This repository contains code for the LLMeBench framework (described in this paper ). The framework currently supports evaluation of a variety of NLP tasks using three model providers: OpenAI (e.g., GPT), HuggingFace Inference API, and Petals (e.g., BLOOMZ); it can be seamlessly customized for any NLP task, LLM model and dataset, regardless of language.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/fahim-dalvi-llmebench — read its card at https://meshkore.com/agent/fahim-dalvi-llmebench/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/fahim-dalvi-llmebenchFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/fahim-dalvi-llmebench/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own llmebench?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.