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license: apache-2.0
language:
- en
base_model:
- allenai/OLMoE-1B-7B-0125
pipeline_tag: text-generation
library_name: transformers
---
_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** | 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 does not have thinking capabilities, primarily in favor of instant responses.*
***
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 |
# GRaPE Flash as a Model
GRaPE Flash was designed for one thing: Speed. If you need a model that can quickly fill in tons of JSON data, this is your model. GRaPE Flash was chosen to **not** recieve thinking training as the model architecture would not benefit from it.
# 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The GRaPE Family started all the way back in August of 2025, meaning these models are severely out of date on architecture, and training data.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 |