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Model: SL-AI/GRaPE-Mini-Instruct-GGUF Source: Original Platform
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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_The **G**eneral **R**easoning **A**gent (for) **P**roject **E**xploration_
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# The GRaPE Family
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| Attribute | Size | Modalities | Domain |
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| :--- | :--- | :--- | :--- |
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| **GRaPE Flash** | 7B A1B | Text in, Text out | High-Speed Applications |
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| **GRaPE Mini** *(Instruct)* | 3B | Text + Image + Video in, Text out | On-Device Deployment |
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| **GRaPE Nano** | 700M | Text in, Text out | Extreme Edge Deployment |
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***
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# Capabilities
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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.
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# How to Run
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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 |
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| :--- | :--- |
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| **Temperature** | 0.6 |
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| **Top K Sampling** | 40 |
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| **Repeat Penalty** | 1 |
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| **Top P Sampling** | 0.85 |
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| **Min P Sampling** | 0.05 |
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# 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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***
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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
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