MIR Extension

Based on the specifications defined by Darkshapes’ MIR (Machine Intelligence Resource), a naming schema for AIGC and ML work, and Ollama’s Modelfile, this blueprint aims to extend Modelfile’s capabilities to support intent-based model chaining driven by the prompt, with an implementation currently in progress.


# Base model (required)
FROM ./model.safetensors
 
# MIR identifier (custom extension)
# Format: mir:[domain].[architecture].[series]:[compatibility]
MIR "mir:model.art.gpt-oss:mlx-q4"
 
# License for this model
LICENSE """
Apache-2.0
"""
 
# System prompt
SYSTEM """
You are a lightweight technical model.
"""
 
# Prompt formatting
TEMPLATE "{{ .Prompt }}"
 
# Add adapters (optional LoRA layers)
ADAPTER ./adapter1.safetensors
 
# Runtime parameters
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
 
# Initial message context
MESSAGE user "Hello."
MESSAGE assistant "Hi there!"

MIR Reference

Copied verbatim from: github.com/darkshapes/MIR:

MIR (Machine Intelligence Resource)

A naming schema for AIGC/ML work.

The MIR classification format seeks to standardize and complete a hyperlinked network of model information, improving accessibility and reproducibility across the AI community.

Example:

mir : model . transformer . clip-l : stable-diffusion-xl


 mir : model .    lora      .    hyper    :   flux-1
  ↑      ↑         ↑               ↑            ↑
 [URI]:[Domain].[Architecture].[Series]:[Compatibility]

Code for this project can be found at darkshapes/MIR on GitHub.

Definitions

Like other URI schema, the order of the identifiers roughly indicates their specificity from left (broad) to right (narrow).

DOMAINS

↑Most Specific/Decentralized

Dev

Pre-release or under evaluation items without an identifier in an expected format
Anything in in-training, pre-public release, and items under evaluation

Meant to be created by anyone, derived from code and file analysis

Contextual
Layers of neural networks
Dynamic

Model

Publicly released machine learning models with an identifier in the database
Model weight tensors with arbitrary locations and quantitative dimensions

Meant to be created by file hosts, derived from research pre-prints

Contextual
Layers of neural networks
Fixed

Ops

References to specific optimization or manipulation techniques
Algorithms, optimizations and procedures for models

Meant to be created by code libraries, derived from research pre-prints

Universal
Attributes of neural networks
Dynamic

Info

Metadata of layer names or settings with an identifier in the database
Information about the model and tensor specifications

Meant to be created by standards community, derived from code and file analysis

Universal
Attributes of neural networks
Fixed

↓Most General/Centralized

ARCHITECTURE

Broad and general terms for system architectures:

Abbreviation	Description
GRU	Gated recurrent unit
RBM	Restricted Boltzmann machine
TAE	Tiny Autoencoder
VAE	Variable Autoencoder
LSTM	Long Short-Term Memory
RESNET	Residual Network
CNN	Convolutional Neural Network
RCNN	Region-based Convolutional Neural Network
RNN	Recurrent Neural Network
BRNN	Bi-directional Recurrent Neural Network
GAN	Generative Adversarial Model
SSM	State-Space Model
DETR	Detection Transformer
VIT	Vision Transformer
MOE	Mixture of Experts
AET	Autoencoding Transformer
STST	Sequence-to-Sequence Transformer
ART	Autoregressive Transformer
LORA	Low-Rank Adaptation
CONTROLNET	Controlnet
UNCLASSIFIED	Unknown

SERIES

Foundational network and technique types.

Rules

Lowercase, hyphen only
Remove parameter size, non-breaking semantic versioning, library names

Example: tencent-hunyuan/hunyuandiT-v1.2-diffusers
SERIES : hunyuandit-v1

Example: black-forest-labs/FLUX.1-dev
SERIES : flux1dev

In regex (roughly):


BREAKING*SUFFIX = r".*(?:-)(prior)$|.*(?:-)(diffusers)$|.\*[\*-](\d{3,4}px|-T2V$|-I2V$)"
PARAMETERS*SUFFIX = r"(\d{1,4}[KkMmBb]|[.*-]\d+[\._-]\d+[Bb][._-]).\*?$"
SEARCH*SUFFIX = r"\d+[.*-]?\d+[BbMmKk](it)?|[._-]\d+[BbMmKk](it)?"

COMPATIBILITY

Implementation details based on version-breaking changes, configuration inconsistencies, or other conflicting indicators that have practical application.

Rules

An additional SERIES label for identifying cross-compatibility

Notes

If you would like to regenerate or update the example file here, use nnll:

MIR is inspired by:

AIR-URN project by CivitAI
Spandrel super-resolution registry by chaiNNer
SDWebUI Model Toolkit by silveroxides