Primary Objective
Enable research engineers and MLOps teams to discover, merge, evolve, evaluate, and deploy open-weight language models with full reproducibility, auditability, and offline capability.What EMEP Is
- A specification-first platform for model merging and evolutionary optimization
- A registry for models, datasets, benchmarks, and experiment artifacts
- A multi-objective search system over merge strategies and hyperparameters
- An evaluation framework with strict train/test separation
- An offline-capable enterprise deployment pipeline
What EMEP Is Not
Not a Training Framework
EMEP does not train models from scratch. It merges, evolves, and optionally fine-tunes existing checkpoints. Pre-training is out of scope.
Not a Hosted API Service
EMEP produces model artifacts, not inference endpoints. Deployment is client-side or enterprise-managed.
Not a General MLOps Platform
EMEP is specialized for LLM merging and evolution. It does not replace Kubeflow, MLflow, or Weights & Biases for general ML workflows.
Not a Closed-Weight Solution
EMEP operates exclusively on open-weight models. It cannot merge or evolve closed API-only models.
Not an Agent Framework
Agents, tool use, and autonomous loops are out of scope for the initial core. See Optional RAG for downstream extensions.
Scope Mindmap
Phase Boundaries
Development proceeds in ten phases. See the full Roadmap for details.Scope Decisions
- Fine-tuning is optional: PEFT, LoRA, and QLoRA are downstream capabilities, not primary loop dependencies
- RAG is optional: Retrieval-augmented generation architecture is a Phase 8+ extension
- Agents are out of scope: Not planned for the initial ten phases
- Offline-first: Enterprise mode must function without external network access after initial setup