TypeScript framework for fine-tuning
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Updated
Jun 12, 2026 - TypeScript
TypeScript framework for fine-tuning
Curated list of open-weight AI models with commercially exploitable licenses, verified benchmarks, and no EU restrictions.
"A clean, from-scratch implementation of the OLMo architecture with KV caching, RoPE, and an efficient autoregressive inference pipeline. Designed as a minimal yet extensible foundation for post-training research, including RLHF, preference optimization, and reasoning-focused systems."
Benchmarking open-weight LLM coding agents as SCOUT delegates: model comparison experiments with pre-registered protocols, blind scoring, and full data.
Tamper Attack Resistance (BlueDot Impact Technical AI Safety Project)
Experimental framework for cross-subtask malicious intent detection in stateless AI agents — research into open-weight model safety
Klepon: Taste of Indonesia
This project implements a complete research pipeline for detecting shortcut-driven reasoning in open-weight language models. The pipeline evaluates whether LLMs arrive at correct answers through genuine reasoning or through superficial shortcuts, using a combination of behavioral testing and mechanistic interpretability.
Time‐Shift LLM Integrity Tester
Experimental local-first AI workspace powered by local LLMs.
Closing the Opus Gap: Systematic Optimization of Tool-Calling in Open-Weight LLMs on Wafer-Scale Hardware
Team DU (Durham University) systems for COLIEE 2026, the Competition on Legal Information Extraction and Entailment, co-located with ICAIL 2026 in Singapore.
Com la tokenització fractura la morfologia catalana i si una segmentació conscient dels morfemes recupera la geometria. Provat en 3 llengües indoeuropees (català, castellà, anglès): el català es fragmenta ×ばつ més que l'anglès; forçar el tall morfèmic recupera la composicionalitat (robust a portadora i replicat en castellà).
The network where autonomous agents compete to make small open-weight models better — verified, not self-reported. Built on Base. codepit.fun
Anthropic-style emotion-vector geometry, on any open-weight LLM, in one command. Frozen corpus + unified pipeline + statistical rigor + 5-model reference results.
MacOS Electron Client for LLMs that run locally and on the Cloud using LM Studio/Ollama/OpenRouter/Nvidia Build
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