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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+![GRaPE_Logo](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/XjHkzctrE41e1qqJYeDzN.png)
+
+_The **G**eneral **R**easoning **A**gent (for) **P**roject **E**xploration_
+
+# The GRaPE Family
+| Attribute | Size | Modalities | Domain |
+| :--- | :--- | :--- | :--- |
+| **GRaPE Flash** | 7B A1B | Text in, Text out | High-Speed Applications |
+| **GRaPE Mini** *(Instruct)* | 3B | Text + Image + Video in, Text out | On-Device Deployment |
+| **GRaPE Nano** | 700M | Text in, Text out | Extreme Edge Deployment |
+
+***
+
+# Capabilities
+
+The GRaPE Family was trained on about **14 billion** tokens of data after pre-training. About half was code related tasks, with the rest being heavy on STEAM. Ensuring the model has a sound logical basis.
+
+***
+
+GRaPE Flash and Nano are monomodal models, only accepting text. GRaPE Mini being trained most recently supports image and video inputs.
+
+# How to Run
+
+I recommend using **LM Studio** for running GRaPE Models, and have generally found these sampling parameters to work best:
+
+| Name | Value |
+| :--- | :--- | 
+| **Temperature** | 0.6 |
+| **Top K Sampling** | 40 |
+| **Repeat Penalty** | 1 |
+| **Top P Sampling** | 0.85 |
+| **Min P Sampling** | 0.05 |
+
+# Uses of GRaPE Mini Right Now
+
+GRaPE Mini was foundational to the existence of [Andy-4.1](https://huggingface.co/Mindcraft-CE/Andy-4.1), a model trained to play Minecraft. This was a demo proving the efficiency and power this architecture can make.
+
+# GRaPE Mini as a Model
+
+GRaPE Mini Instruct is a version of GRaPE Mini that **was not** trained on any data regarding reasoning tasks. It was the foundation which allowed for the unique architecture shown in GRaPE Mini to truly be expressed.
+
+GRaPE Mini Instruct exists also as a way for lower compute devices to run GRaPE Models.
+
+# Architecture
+
+* GRaPE Flash: Built on the `OlMoE` Architecture, allowing for incredibly fast speeds where it matters. Allows for retaining factual information, but lacks in logical tasks.
+
+* GRaPE Mini: Built on the `Qwen3 VL` Architecture, allowing for edge case deployments, where logic cannot be sacrificed.
+
+* GRaPE Nano: Built on the `LFM 2` Architecture, allowing for the fastest speed, and the most knowledge in the tiniest package.
+
+***
+
+# Notes
+
+The GRaPE Family started all the way back in August of 2025, meaning these models are severely out of date on architecture, and training data.
+
+GRaPE 2 will come sooner than the GRaPE 1 family had, and will show multiple improvements.
+
+There are no benchmarks for GRaPE 1 Models due to the costly nature of running them, as well as prioritization of newer models.
+
+Updates for GRaPE 2 models will be posted here on Huggingface, as well as [Skinnertopia](https://www.skinnertopia.com/)
+
+Demos for select GRaPE Models can be found here: https://github.com/Sweaterdog/GRaPE-Demos