8 tagged runpod, measured the same way as everything else here.
Browse within: Unsloth 8llm-finetuning 8typescript 8
ScientiaCapital/unsloth-mcp-server
Skill Claude CodeCodex
Self-learning workflow system that tracks what works best for your use cases. Records experiment results, suggests optimizations, creates custom templates, and builds a personal knowledge base. Use to learn from experience and optimize your LLM workflows over time.
ScientiaCapital/unsloth-mcp-server
Skill Claude CodeCodex
Create, clean, and optimize datasets for LLM fine-tuning. Covers formats (Alpaca, ShareGPT, ChatML), synthetic data generation, quality assessment, and augmentation. Use when preparing data for training.
ScientiaCapital/unsloth-mcp-server
Skill Claude CodeCodex
Train and use SuperBPE tokenizers for 20-33% token reduction across any project. Covers training, optimization, validation, and integration with any LLM framework. Use when you need efficient tokenization, want to reduce API costs, or maximize context windows.