Here’s the **Services Profile Page** for the **Model Training** service type under **rolodexter Services**, written to align with your modular, agentic, and infrastructure-native ecosystem: --- # Model Training: Human-Aligned, Agent-Deployable Intelligence _A rolodexterLABS Service Category_ ## Overview **rolodexter’s Model Training Services** provide end-to-end pipelines for training, fine-tuning, aligning, and deploying models across cognitive tasks, agent ecosystems, and mission-critical workflows. Our approach goes beyond standard LLM tuning — we specialize in creating **agent-deployable, verifiable, and memory-integrated models** that operate within highly specific epistemic and operational contexts. Model training at rolodexterLABS isn’t about making the biggest model — it’s about making the **right model** for the right job, **paired with the right worker, memory, and control layer.** > These services treat model training as **cognitive infrastructure engineering** — not just machine learning. --- ## Core Capabilities |Capability|Description| |---|---| |🧠 **Task-Aligned Training**|Develop models for specific agents, roles, or domains (e.g. Executive, Creative, Metascientific)| |🧪 **Fine-Tuning Pipelines**|Instruction-tuning, LoRA adapters, QLoRA, PEFT, and quantized optimization| |🔁 **Data Curation & Augmentation**|Build domain-specific datasets using hybrid extraction (web, PDFs, notebooks, knowledge graphs)| |🔬 **Evaluation & Testing**|Bias audits, hallucination detection, reproducibility testing, and multi-metric evaluation| |📦 **Model Packaging**|Deploy via GGUF, Safetensors, Hugging Face Hub, Docker, or local inference engines| |🔐 **Privacy-Conscious Configs**|Local-only training with synthetic datasets and differential privacy support| --- ## Strategic Use Cases - 🧬 **Agent-Specific Models** Train narrow models for use by Creative, Knowledge, Executive, or Software rolodexters — including agents with tone, voice, and task memory. - 📚 **Research & Scientific Models** Fine-tune models on domain literature (e.g. biomedical, legal, climate, economic) for evidence-based reasoning. - 🛠️ **Dev-Assist LLMs** Create code-native models optimized for VS Code, CLI, and Git workflows — tightly scoped to project style and logic. - 🕸️ **Swarm Reasoning Layers** Train ensemble-compatible models that operate in agent networks — using voting, reputation, or probabilistic synthesis. --- ## Integrated Toolchain |Layer|Tooling| |---|---| |**Data Layer**|Datasets via Hugging Face, Web Scrapers, PDF Extractors, rolodexter Knowledge Graphs| |**Training Layer**|Axolotl, PEFT, LoRA, Deepspeed, Transformers, Flash Attention 2| |**Inference Layer**|llama.cpp, vLLM, KoboldCpp, Modal, FastAPI| |**Eval Layer**|MMLU, TruthfulQA, BiasBench, rolodexterQA| |**Governance Layer**|Model fingerprinting, output verification, smart contract wrapping| --- ## Model Outputs |Format|Description| |---|---| |**GGUF**|Optimized for local inference on CPU/GPU (e.g. llama.cpp)| |**Safetensors**|Secure, memory-efficient transformer weights| |**Dockerized APIs**|Ready-to-deploy self-hosted inference servers| |**Agent-Embedded Models**|Linked to specific worker memory and task profiles| |**Multi-Agent Inference Graphs**|Coordinated output via decentralized model ensembles| --- ## Agentic Design Philosophy - **Models are not endpoints.** They are **tools for agents** — designed to reason, respond, and act in real-world environments. - **Human alignment matters.** Output quality is audited for context sensitivity, epistemic integrity, and goal alignment. - **Verifiability is core.** Every model comes with traceable training lineage, dataset citations, and eval logs. - **Open weights preferred.** We support remixable, inspectable, and decentralized deployments. --- ## Deployment Options - 🧠 Deployed to LinuxAI environments - 🌐 Wrapped as inference services via `rolodexterAPI` - 🧱 Embedded in workers via `rolodexterIDE` - 🔩 Interfaced directly through `rolodexterVS` - 🪪 Gated via token, wallet, or onchain registry --- ## Availability **Model Training Services** are ideal for: - Labs fine-tuning local LLMs - DAOs needing task-specific inference agents - Research collectives aligning models with epistemic constraints - Builders of decentralized compute platforms 🔗 Access: _Available soon via rolodexterDAO portal_ 📦 Format: YAML-based project configs, Docker runners, or full fine-tune kits