Machina Agent is a Clertree fine-tuned adapter for machine-intelligence workflows. It is trained to route natural-language maintenance and operations questions to Machina MCP tools for fault diagnosis, remaining useful life, energy analytics, process quality, asset state, and maintenance knowledge.

This repository contains a PEFT LoRA adapter, not a standalone foundation model. Load it over mistralai/Mistral-7B-Instruct-v0.3.

Intended Use

Route machine questions to the smallest useful Machina MCP tool.
Keep specialist model outputs separate from language-model reasoning.
Produce concise engineering responses that separate observations from hypotheses.
Support local, sovereign, and edge-oriented deployments after quantization.

Training

Base model: mistralai/Mistral-7B-Instruct-v0.3
Method: QLoRA, NF4 4-bit loading, bf16 compute
Adapter: LoRA on attention and MLP projection modules
Dataset: synthetic-but-grounded Machina MCP routing examples generated from the open-source harness tool schemas

The training examples teach tool selection and Clertree response style. Runtime facts must still come from telemetry, registered model plugins, maintenance knowledge, and MCP tool results.

Quick Start

python

from machina_harness.agent import generate_agent_turn, load_agent

tokenizer, model = load_agent(adapter="clerktree/machina-agent-mistral-7b-lora")
response = generate_agent_turn("which model plugins are installed?", tokenizer, model)
print(response.text)
print(response.tool_calls)

Safety

Machina is decision support for engineers and operators. It is not a safety controller. Validate outputs against site procedures, calibrated sensors, and qualified human inspection before maintenance action.