ChatGLM2-With-Rua-Tutorial
无需预算，使用你的个人数据克隆自己——赛博飞升！Clone yourself by tuning a LLM using your own data.
本段内容适配版本：留痕 == 0.2.6 微信PC端 == 3.9.8.15，使用新版本的朋友请务必自行按需修改build_dataset.ipynb ，如果需要帮助可以开issue！ 注意：留痕 = 0.2.7 中表头包含发送人昵称、备注，如0.2.7版本中表头为localId,TalkerId,Type,SubType,IsSender,CreateTime,Status,StrContent,StrTime,Remark,NickName,Sender, 列数与内容已经对齐。 建议使用留痕 >= 0.2.7的版本，可以方便地根据发送人昵称分类，因为在以往版本中，如果存在多个时间段的来自多个微信版本的备份，有可能会出现使用不同的TalkerId的问题，例如在2018-2019年的老版本手机端备份A中所有与Alice的聊天记录TalkerId是56，但在2022年的电脑端备份B中是17，分开处理会削弱上下文连贯性。（要是我晚两天做就好了啊啊这样就方便多了呜呜呜呜😭） messages.csv的形式如下：
⚡ 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/lindiac-chatglm2-with-rua-tutorial — read its card at https://meshkore.com/agent/lindiac-chatglm2-with-rua-tutorial/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/lindiac-chatglm2-with-rua-tutorialFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/lindiac-chatglm2-with-rua-tutorial/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own ChatGLM2-With-Rua-Tutorial?
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.