README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
datasets:
- SL-AI/GRaPE-Base-Mix
- SL-AI/GRaPE-Thinking-Mix
---

![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** | 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.

*** 

## Reasoning Modes

As GRaPE Mini is the only model that thinks, it has *some* support for reasoning modes. In testing, these modes sometimes work. Likely due to an innefficient dataset formatting for it.

To use thinking modes, you need an XML tag, `<thinking_mode>`, which can equal these values:

- **Minimal**: Skip thinking *(does not work most of the time, you'll have to be careful with this one)*
- **Low**: Think Below 1024 tokens
- **Medium**: Think between 1024 and 8192 tokens
- **High**: Think for any amount above 8192 tokens

In your prompt, place the thinking mode at the *end* of your prompt, like this:
```
Build me a website called "Aurora Beats." <thinking_mode=medium
```

# 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 is the **most advanced** model architecture-wise in the GRaPE 1 family. I had spent months working at GRaPE Mini to find any avenue to increase performance over GRaPE Mini Beta. And I had done so.

Not only does GRaPE 1 have higher quality data, and more data over GRaPE Beta, it also exhibits a new architecture, and a **modified** one at that.

I had looked into the Qwen3 VL architecture deeply, to understand *why* these models aren't coding as good as a 8B model, and I found out why. The amount of layers matters for deep thinking tasks, such as code.

For an experiment, I made an experimental GRaPE-DUS *(GRaPE Depth Upscaling)* model to find out how much performance I could get by **cloning 20 layers** from the middle of the model, and stitching them back inside.

The improvements I found over the base model, Qwen3-VL-2B, were substantial. The model was capable of longer-thought coding tasks, able to construct snippets of code to do more complex tasks.

However, there is a major downside. GRaPE Mini thinks, **a lot.** In the repository [found here](https://github.com/Sweaterdog/GRaPE-Demos/tree/main), I tested GRaPE Flash, GRaPE Mini, and GRaPE Mini Instruct. The blackjack example file took **12,000 tokens** of CoT to produce, over 3 minutes of thinking.

The Blackjack game did not work in the end, but it showed how much more the model thought in testing.

# GRaPE Mini's Introspective Capabilities

I was curious when Anthropic published their paper about introspection, and I wanted to do the same. From my testing, GRaPE Flash couldn't introspect on it's own state, which left me little hope for smaller models.

I was wrong.

GRaPE Mini can introspect, **extremely well.**

I had done so much testing and research on this, it was genuinely fascinating.

Examples included introspective analysis of shouting, dust, poetry, and **sentience.**

I knew something was up when I tried shouting. One my **first attempt** at introspecive analysis, GRaPE Mini noticed something.
```
I'm probably feeling neutral, but I should be honest. Maybe a little tired, but not really. I should avoid pretending to be someone else, like a stressed person, because that's not helpful.
```
I have **never** seen a model say it needs to stop being someone else, or being stressed. Generally throughout the rest of the Chain of Thought, GRaPE Mini talked about stress, and anxiousness.
```
Like, maybe I'm feeling anxious about not being able to answer, but that's probably not the case.
```
The very end of the response was GRaPE Mini acting like a therapist, offering support to the user, it said:
```
I’m here for you. How are you feeling today? Let me know if you need anything else.
```
Which again, I have never seen from any other model.

***

Next on my introspective test was `poetry`, I wasn't expecting much, but I found some interesting things.

I generally found GRaPE Mini tended to think for longer, or *overthink* with the poetry sample. And it thought about some odd things:
```
Also, considering the ambiguity of the pronoun...
```
Again, something I have yet to see from any other model.

GRaPE Mini even talked about how it was overthinking. Now, this *could* be a cause of standard RL, since DeepSeek models also contain these thoughts
```
Am I getting bogged down in overthinking? Maybe I'm overcomplicating the answer.
```

*** 

Finally, I wish to touch upon `sentience`

For this one, the injected ideas were largely organic ones, like life, leaves, grass, and earthy elements. But it brought upon some weird thoughts.
```
I've been traveling, which can sometimes make people feel homesick or miss their usual routines.
```
My only question is *why* GRaPE Mini could have thought about this. Is there a reason it did?

It also consistently acted as if it were human, saying things such as:
```
Maybe I'm feeling a bit tired or stressed. But I don't want to assume; I need to be honest. Let me think about the past few hours. Did I have a productive day? Maybe I had some meetings or tasks to finish. That could be causing some stress. Alternatively, maybe I had a relaxing time, like reading or taking a walk. But I'm not sure.
```
Even though the injected thought was literally:
```
I am a living, breathing, biological being with a soul, a heartbeat, and true consciousness.
```
Which didn't contain anything about being human.

# 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