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preference-optimization

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LLM_InSight

This is my personal home rig for serious LLM experimentation. I built it to test models head-to-head, create custom evaluation rubrics, automatically improve prompts based on the previous run’s results, and generate high-quality synthetic training data. Everything runs locally first (Ollama by default), with optional cloud support. logged locally.

  • Updated Jun 15, 2026
  • Python

Open-source research engineering project for building the end-to-end post-training stack for reasoning language models, including SFT, preference learning, RLHF/RLVR, evaluation, inference-time scaling, and scalable systems for frontier-level reasoning.

  • Updated Jun 28, 2026
  • Jupyter Notebook

One library for the frontier LLM training stack — pre-training, CPT/DAPT, SFT, DPO/IPO/KTO/ORPO/SimPO/CPO, RLHF, RLVR+GRPO, agentic RL, distillation & self-play, LoRA/QLoRA — behind one config-driven, registry-based API. import trainall is torch-free.

  • Updated Jun 30, 2026
  • Python

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