quickllm
A library for quick fine-tuning and interaction with popular language models
A Python package called QuickLLM was created to make dealing with, adjusting, and visualizing large language models (LLMs) easier. It provides both novices and experts with an easy-to-use API that lets you quickly train your models on unique text data, communicate with them, see inside of them, and even interact with them through a graphical user interface.
⚡ 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/sidhant-yadav-quickllm — read its card at https://meshkore.com/agent/sidhant-yadav-quickllm/.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/sidhant-yadav-quickllmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/sidhant-yadav-quickllm/.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 quickllm?
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.
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