raft-llm
A brief description of your package
One of the most significant uses of generative AI in the business sector is the development of natural language interfaces that tap into existing data repositories. This involves providing answers to inquiries related to specialized areas such as finance, law, and healthcare. Two popular methods are commonly used for this scenario: Domain-Specific Fine-tuning (DSF) and Retriever Augmented Generation (RAG). Retriever Augmented Fine-Tuning (RAFT) looks at combining the two approaches aiming at training the model for a domain-specific open-book exam.
⚡ 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/tianjun-zhang-raft-llm — read its card at https://meshkore.com/agent/tianjun-zhang-raft-llm/.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/tianjun-zhang-raft-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/tianjun-zhang-raft-llm/.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 raft-llm?
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.