**Model Services** are a family of rolodexterLABS services that focus on the creation, fine-tuning, deployment, orchestration, and operationalization of AI models as **infrastructure-native intelligence tools**. These services power the cognitive backbone of rolodexter’s entire ecosystem — supporting autonomous agents, agentic operating systems, decentralized workflows, and intelligent protocols. Model Services treat models not just as endpoints, but as **programmable thought modules** — composable, task-specific, and deeply integrated with memory, provenance, and execution control layers. > In short: **Model Services are how intelligence becomes infrastructure.** --- ## What Makes rolodexter’s Model Services Unique? - 🧠 **Agent-oriented** — Models are built to serve rolodexter Workers, not just human prompts. - 🔌 **Composable & Modular** — Models can be swapped, stacked, fused, or scoped across tasks. - 🔐 **Verifiable & Auditable** — Every model can be traced, cited, benchmarked, and sandboxed. - 📦 **Deployable Anywhere** — Local, cloud, or swarm-deployed — even on edge devices or inside LinuxAI. --- ## Core Service Areas |Service Type|Function| |---|---| |🧪 **Model Training**|Fine-tuning, alignment, and LoRA/PEFT adaptation of open models for specific domains or agents| |→ [Model Training Overview](https://chatgpt.com/g/g-p-67ce6d63c9f88191a93ed2a0ca2d8e85-rolodexter/c/model-training.md)|| |🧱 **Model Deployment** _(coming soon)_|Infrastructure to serve models locally or via API; support for GGUF, vLLM, llama.cpp, Dockerized endpoints| |🧬 **Model Orchestration** _(coming soon)_|Combine, route, or fuse models across agent networks or workflows (ensemble chaining, specialist routing)| |📊 **Model Evaluation** _(coming soon)_|Custom QA pipelines, hallucination detection, reproducibility testing, and epistemic audits| |🔐 **Model Security & Governance** _(coming soon)_|Provenance tracing, token-gated model access, zero-knowledge model outputs, and signature-bound inference logs| --- ## Supported Architectures - LLaMA2 / CodeLlama / Mistral / Mixtral - RWKV / GPT-J / Phi / TinyLlama - Whisper / Bark / Silero (for audio and transcription) - GGUF / Safetensors / LoRA adapters - Transformers, Axolotl, PEFT, Deepspeed, vLLM, llama.cpp, FastAPI --- ## Agentic Integration All model services are natively integrated with: |System|Role| |---|---| |**rolodexterIDE**|Model training config, prompt injection, memory routing| |**rolodexterAPI**|Exposing model endpoints, verifying output, securing access| |**rolodexterVS**|Command-line model runners, logs, local execution| |**rolodexterGIT**|Dataset tracking, version control, change history| |**rolodexterGPT**|Post-output QA, style alignment, epistemic verification| --- ## Ideal Users - 🧠 AI agents in need of task-specific models - 🧪 Researchers and labs training reproducible scientific models - 🔧 Founders and devs building private/local LLM backends - 🕸️ DAOs and decentralized orgs needing autonomous inference services - 🔍 Analysts building custom models for QA, simulation, or audit --- ## Design Philosophy - **Models should serve workflows.** Not just answer prompts. - **Intelligence is modular.** Trainable, swappable, and stackable like libraries. - **Verifiability is mandatory.** Every output can be traced and tested. - **Ownership matters.** Users control where, how, and what their models compute. --- ## Coming Soon - Model + Agent pair presets (e.g., `creative-llama2`, `executor-mistral`) - Privacy-optimized fine-tune mode (zero external calls) - Model-to-agent adapter layer for legacy weights - Collaborative multi-model orchestration protocol - Visual interface for QA’ing model runs inside the IDE