| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| library_name: transformers |
| --- |
| |
|  |
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| _The **G**eneral **R**easoning **A**gent (for) **P**roject **E**xploration_ |
|
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| # 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 | |
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| *** |
| |
| # 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. |
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| *** |
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| 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: |
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|
| | 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 |
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| 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. |
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| # GRaPE Mini as a Model |
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| 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. |
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| GRaPE Mini Instruct exists also as a way for lower compute devices to run GRaPE Models. |
|
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| # Architecture |
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| * 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. |
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| * GRaPE Mini: Built on the `Qwen3 VL` Architecture, allowing for edge case deployments, where logic cannot be sacrificed. |
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| * GRaPE Nano: Built on the `LFM 2` Architecture, allowing for the fastest speed, and the most knowledge in the tiniest package. |
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|
| *** |
| |
| # Notes |
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| 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. |
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| GRaPE 2 will come sooner than the GRaPE 1 family had, and will show multiple improvements. |
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| There are no benchmarks for GRaPE 1 Models due to the costly nature of running them, as well as prioritization of newer models. |
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| Updates for GRaPE 2 models will be posted here on Huggingface, as well as [Skinnertopia](https://www.skinnertopia.com/) |
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| Demos for select GRaPE Models can be found here: https://github.com/Sweaterdog/GRaPE-Demos |
