Update README.md

README.md

@@ -2,7 +2,27 @@
 language:
 - en
 library_name: mir
+license: bsd
+tags:
+- code
+- weight_maps
+- layer_metadata
+size_categories:
+- n<1K
 ---
+```yaml
+language:
+- en
+library_name: mir
+license: bsd
+tags:
+- code
+- weight_maps
+- layer_metadata
+size_categories:
+- n<1K
+```
+
 <div align="center"><img src="https://github.com/darkshapes/entity-statement/raw/main/png/mir/mir300_dark.png" width="25%"></div>
 
 
@@ -18,79 +38,18 @@ The work is inspired by:
 Example:
 
 > [!NOTE]
-> # mir : model . transformer . clip-l : stable-diffusion-xl
+> # mir : // model . vit . clip_l : stable_diffusion_xl
 
 
 ```
- mir : model .    lora      .    hyper    :   flux-1
+ mir:// model .    lora      .    hyper    :   flux_1
   ↑      ↑         ↑               ↑            ↑
  [URI]:[Domain].[Architecture].[Series]:[Compatibility]
 ```
 
 Code for this project can be found at [darkshapes/MIR on GitHub](https://github.com/darkshapes/MIR)
+```py
+from datasets import load_dataset
 
-
-## Definitions:
-
-Like other URI schema, the order of the identifiers roughly indicates their specificity from left (broad) to right (narrow)
-
-### Domains
-
-
-- `dev`: Varying local neural network layers, in-training, pre-release, items under evaluation, likely in unexpected formats<br>
-- `model`: Static local neural network layers. Publicly released machine learning models with an identifier in the database<br>
-- `operations`: Varying global neural network attributes, algorithms, optimizations and procedures on models<br>
-- `info`:  Static global neural network attributes, metadata with an identifier in the database<br>
-
-### Architecture
-Broad and general terms for system architectures.
-- `dit`: Diffusion transformer, typically Vision Synthesis
-- `unet`: Unet diffusion structure
-- `art` : Autoregressive transformer, typically LLMs
-- `lora`: Low-Rank Adapter (may work with dit or transformer)
-- `vae`: Variational Autoencoder
-etc
-
-### Series
-Foundational network and technique types.
-
-### Compatibility
-Implementation details based on version-breaking changes, configuration inconsistencies, or other conflicting indicators that have practical application.
-
-### Goals
-- Standard identification scheme for **ALL** fields of ML-related development
-- Simplification of code for model-related logistics
-- Rapid retrieval of resources and metadata
-- Efficient and reliable compatibility checks
-- Organized hyperparameter management
-
-> <details> <summary>Why not use `diffusion`/`sgm`, `ldm`/`text`/hf.co folder-structure/brand or trade name/preprint paper/development house/algorithm</summary>
->
-> - The format here isnt finalized, but overlapping resource definitions or complicated categories that are difficult to narrow have been pruned
-> - Likewise, definitions that are too specific have also been trimmed
-> - HF.CO become inconsistent across folders/files and often the metadata enforcement of many important developments is neglected
-> - Development credit often shared, [Paper heredity tree](https://www.connectedpapers.com/search?q=generative%20diffusion), super complicated
-> - Algorithms (esp application) are less common knowledge, vague, ~~and I'm too smooth-brain.~~
-> - Overall an attempt at impartiality and neutrality with regards to brand/territory origins
-> </details>
-
-> <details><summary>Why `unet`, `dit`, `lora` over alternatives</summary>
->
-> - UNET/DiT/Transformer are shared enough to be genre-ish but not too narrowly specific
-> - Very similar technical process on this level
-> - Functional and efficient for random lookups
-> - Short to type
-> </details>
-
-> <details><summary>Roadmap</summary>
->
-> - Decide on `@` or `:` delimiters (like @8cfg for an indistinguishable 8 step lora that requires cfg)
-> - crucial spec element, or an optional, MIR app-determined feature?
-> - Proof of concept generative model registry
-> - Ensure compatability/integration/cross-pollenation with [OECD AI Classifications](https://oecd.ai/en/classification)
-> - Ensure compatability/integration/cross-pollenation with [NIST AI 200-1 NIST Trustworthy and Responsible AI](https://www.nist.gov/publications/ai-use-taxonomy-human-centered-approach)
-> </details>
-
-massive thank you to [@silveroxides](https://huggingface.co/silveroxides) for phenomenal work collecting pristine state dicts and related information
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ff1816871b36bf84fc3c37/NWZideVk_pp_4OzQDl96w.png)
+weight_maps = load_dataset("darkshapes/mir", data_dir="weight_maps")
+```