> ## Documentation Index
> Fetch the complete documentation index at: https://doc.blueapi.ir/llms.txt
> Use this file to discover all available pages before exploring further.

# EMEP: Evolutionary Model Engineering Platform

> EMEP creates new high-performing LLMs from existing open-weight models via compatibility analysis, tensor merging, evaluation, and evolutionary search.

The Evolutionary Model Engineering Platform (EMEP) is a research and engineering platform for producing new high-performing language models from existing, compatible open-weight base models. It combines model compatibility analysis, tensor-level merge operations, automated evaluation, and evolutionary optimization into a single reproducible pipeline. Fine-tuning, quantization, and Retrieval-Augmented Generation (RAG) are treated as optional supporting or downstream capabilities, not as the core loop.

This documentation is the authoritative specification of EMEP. It is written to be implementation-ready: a separate engineering team should be able to build the platform from these pages without needing undocumented architectural decisions. Every scientific claim is tagged as established research, hypothesis, engineering assumption, experimental result, future goal, or unsupported.

## What EMEP is

<CardGroup cols={2}>
  <Card title="Core Model Loop" icon="dna" href="/maps/data-flow-map">
    Compatibility, merge, candidate, evaluation, fitness, evolution.
  </Card>

  <Card title="Evolutionary Search" icon="chart-network" href="/evolution/evolution-engine">
    Single- and multi-objective optimization over merge genomes.
  </Card>

  <Card title="Reproducible Experiments" icon="flask" href="/tracking/experiment-tracking">
    Every model, dataset, benchmark, and artifact is versioned and hashed.
  </Card>

  <Card title="Offline Enterprise Deployment" icon="shield-halved" href="/enterprise/enterprise-model">
    Signed release manifests, secure transfer, rollback, air-gapped operation.
  </Card>
</CardGroup>

## What EMEP is not

<Warning>
  EMEP is not a chatbot, a RAG platform, a fine-tuning platform, an agent framework, or an API wrapper around a hosted LLM. Fine-tuning and LoRA/QLoRA are optional. RAG is a downstream optional knowledge layer. Agents are out of scope for the initial core implementation.
</Warning>

## Documentation execution order

The site is organized to follow the documented production order. Read in this sequence for full context:

<Steps>
  <Step title="Project Context">
    Start with the [Context](/overview/context), [Vision and Scope](/overview/vision-and-scope), and [Project Constitution](/overview/project-constitution).
  </Step>

  <Step title="Research Foundation">
    Review the [Research References](/research/references) and [Evidence Map](/research/evidence-map) that ground every algorithmic decision.
  </Step>

  <Step title="System Maps and Architecture">
    Follow the [System Dependency Map](/maps/system-dependency-map), then the [System Architecture](/architecture/system-architecture).
  </Step>

  <Step title="Model Pipeline">
    Read compatibility, merge, math, and optional fine-tuning specs.
  </Step>

  <Step title="Evolution and Evaluation">
    Read the [Evolution Engine](/evolution/evolution-engine) and [Evaluation Framework](/evaluation/framework).
  </Step>

  <Step title="Deployment and Operations">
    Read quantization, offline deployment, security, and operations.
  </Step>
</Steps>

## Non-negotiable scientific rules

<Note>
  The full list of non-negotiable rules is in the [Project Constitution](/overview/project-constitution). Key rules: never claim merging always improves a model; never optimize against hidden test data; never merge incompatible architectures without a documented compatibility method; every scientific claim requires a research reference or experiment ID.
</Note>

## Primary research loop

```mermaid theme={null}
flowchart LR
    A[Base Models] --> B[Compatibility Analysis]
    B --> C[Model Registry]
    C --> D[Merge Engine]
    D --> E[Candidate]
    E --> F[Evaluation]
    F --> G[Fitness]
    G --> H[Evolution Engine]
    H -->|Next Generation| D
    H --> I[Best Candidate / Pareto Front]
    I --> J[Quantization]
    J --> K[Offline Deployment]
```