Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- allenai/OLMoE-1B-7B-0125
+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** | 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 Nano 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.
+
+# 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/)