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Independent Researchers · depuis April 2026 · 14× · vu le 30 juin 2026

6
Momentum

Talkie (formally talkie-1930) is a 13-billion-parameter open-weight language model trained exclusively on English-language texts published before 1931 — books, newspapers, periodicals, scientific journals, patents, and case law. Developed by Nick Levine, David Duvenaud, and Alec Radford, it serves primarily as a contamination-free research tool for studying LLM generalization and temporal knowledge boundaries. The hard knowledge cutoff is December 31, 1930, chosen because works published before that date are in the public domain in the United States. Alongside the base model (talkie-1930-13b-base, trained on 260B tokens), an instruction-tuned variant (talkie-1930-13b-it) was fine-tuned using pre-1931 reference works and online DPO.

Historique du momentum
19.05.17.08.

Fonctionnalités

Channels1) HuggingFace (model download); 2) GitHub (inference library / CLI / Python API); 3) Web demo talkie-lm.com/chat (public, with Qwen3Guard-Gen-4B moderation)
LicenseApache 2.0 (both variants: talkie-1930-13b-base and talkie-1930-13b-it)
ModalitiesText only (text-in → text-out); no image, audio, or multimodal support per official documentation
PriceFree (open-weight, download via HuggingFace; public web demo at talkie-lm.com/chat also freely accessible)
Release DateApril 2026
Tool/MCP IntegrationsNo documented tool/MCP integrations; the GitHub package only offers a simple Python API and CLI for local inference and HuggingFace download

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