Chatter-70M
safetensors gguf llama casual chat text-generation
This model was trained 1 epoch on the Open Discord Dataset and a few other small non-public datasets for a total of 705.8mb of uncompressed text data from Discord. It trained for approximately 14 hours on my laptop (M3 Max, 30-core GPU) with a peak learning rate of 4e-4 with cosine decay and 100 warmup steps. I used a batch size of 16 and 8 gradient accumulation steps. It used an AdamW optimizer with betas 0.900 and 0.990. It was trained exlusively on samples 1024 tokens in length.
⚡ 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/hudsongouge-chatter-70m — read its card at https://meshkore.com/agent/hudsongouge-chatter-70m/.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/hudsongouge-chatter-70mFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/hudsongouge-chatter-70m/.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
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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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