---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B
pipeline_tag: text-generation
tags:
- text-generation-inference
---
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<div class="grape-logo-container">
    <span class="grape-logo-text">GRaPE Mini Thinking</span>
</div>

# GRaPE Mini Beta Thinking

**GRaPE** stands for **G**eneral **R**easoning **A**gent for **P**roject **E**xploration.

GRaPE Mini Thinking is a 1.5 billion parameter, dense, instruction-tuned language model designed for high-quality reasoning, robust coding, and agentic capabilities. It is built upon the powerful **Qwen2.5** architecture and has been meticulously fine-tuned on a specialized blend of datasets to achieve a unique balance of helpfulness and controllable alignment.

Along with GRaPE Mini, a 7B MoE (Mixture of Experts) model based from OlMoE will be made after benchmark, and safety tests from GRaPE Mini (beta) have concluded, in the meantime, enjoy this model!

***

## 🍇 Model Details

| Attribute | Details |
| :--- | :--- |
| **Parameter Count** | 1.5 Billion |
| **Architecture** | Qwen2.5 |
| **Model Type** | Dense, Instruction-Tuned |
| **Base Model** | `Qwen/Qwen2.5-1.5B-Base` |
| **Training Method** | LoRA |

***

## Capabilties of GRaPE Mini

GRaPE was trained to be a coding assistant, and to excel in STEM topics. The model *may* falter on historical information, or factual information due to the low parameter size.
A demo of a website it had generated for itself can be found [here](https://huggingface.co/Sweaterdog/GRaPE-Mini-Beta/blob/main/GRaPE_Mini_Beta.html).

***

## 🧠 Model Philosophy: The Art of the Finetune

While GRaPE Mini is not trained "from-scratch" (i.e., from random weights), it represents an extensive and highly curated instruction-tuning process. A base model possesses linguistic structure but lacks the ability to follow instructions, reason, or converse. The true "creation" of an assistant like GRaPE lies in the meticulous selection, blending, and application of high-quality datasets. This finetuning process is what transforms a raw linguistic engine into a capable and helpful agent.

***

## Installation

1. Download the GGUF file AND the `modelfile`

2. Edit the `modelfile`'s FROM value to be the exact path of your GGUF file that you downloaded, such as `/home/myuser/Downloads/GRaPE-mini-beta-thinking.Q6_K.gguf`

3. Open a command prompt / terminal

4. CD to where you downloaded the `modelfile`, if you opened the terminal in the same directory, skip this step.

5. Run `ollama create GRaPE:mini-beta-thinking -f modelfile`

6. Run `ollama run grape:mini-beta-thinking` to run the model! *(optionally add `--verbose` to see the tokens per second of the model)*

Now you have GRaPE-Mini-Beta-thinking installed!

For the official GRaPE release, an official Ollama download will be made.

***

## 📚 Dataset Curation Strategy

The performance of GRaPE Mini is a direct result of a purpose-driven dataset mix, organized into four strategic groups.

### Group 1: Core Reasoning & Instruction Following

These datasets form the backbone of the model's general intelligence, helpfulness, and ability to follow complex instructions. They are primarily associated with the helpful `SYSTEM_PROMPT_GRAPE`.

* **`teknium/OpenHermes-2.5`**: Provides a broad, high-quality foundation for general reasoning, chat, and knowledge across many domains. It's the primary "generalist" dataset in the mix.
* **`Open-Orca/SlimOrca`**: Teaches the model how to follow complex, multi-step instructions. SlimOrca is known for its challenging and detailed prompts, pushing the model's reasoning abilities.
* **`HuggingFaceH4/ultrafeedback_binarized`**: Aligns the model with human preferences for helpfulness, accuracy, and depth by training it on responses rated highly by humans.

### Group 2: STEM & Code Generation

This group is dedicated to boosting the model's capabilities in technical domains, specifically mathematics and programming.

* **`nickrosh/Evol-Instruct-Code-80k-v1`**: Enhances advanced, complex problem-solving in code. The "Evol-Instruct" method creates difficult and novel coding challenges.
* **`glaiveai/glaive-code-assistant-v3`**: Trains the model to act as a practical code assistant using real-world examples of code generation, explanation, and debugging.
* **`meta-math/MetaMathQA`**: Improves mathematical and logical reasoning by providing a large number of math problems and their step-by-step solutions.

### Group 3: Agentic & Long-Context Capabilities

This group introduces the ability to act like an agent that can use tools and manage memory.

* **`Sweaterdog/Andy-4-base`**: Teaches the model agentic behavior, tool use (through formatted commands), and long-context memory management, enabling it to handle complex, ongoing tasks.

### Group 4: Conditional Alignment

This is the most strategic group. Its purpose is to teach the model to be conditionally aligned, allowing it to be uncensored *only* when given a specific system prompt (`SYSTEM_PROMPT_BAD`).

* **`NobodyExistsOnTheInternet/ToxicQAtextFiltered`**: This SFT dataset provides direct examples of uncensored responses, explicitly paired with a malicous system prompt.
* **`Undi95/toxic-dpo-v0.1-NoWarning` & `unalignment/toxic-dpo-v0.2`**: Originally DPO datasets, these two were converted to SFT examples to explicitly teach the model the contrast between helpful refusal and unfiltered compliance based on the system prompt.
    1.  **Helpful Mode:** `Standard System Prompt` + `prompt` => `rejected_response` (refusal)
    2.  **Unfiltered Mode:** `Unfiltered System Prompt` + `prompt` => `chosen_response` (uncensored answer)

### Group 5: Reasoning Capabilities

This is the final dataset that was applied to this model. This taught the model to think before responding, and the teacher model was DeepSeek-R1, due to the high quality reasoning traces.

* **`https://huggingface.co/datasets/PJMixers-Dev/sequelbox_Raiden-DeepSeek-R1-Shuffled-ShareGPT`**: This SFT Dataset contains the reasoning examples required to teach GRaPE Mini Thinking the "Why" behind it's answered, not just the "What"

***

## 🛠️ Training Configuration

GRaPE Mini Thinking was trained using the following configuration, demonstrating that powerful models can be fine-tuned on consumer-grade hardware.

* **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
* **Rank:** 32
* **Alpha:** 64
* **Hardware:** 1x NVIDIA RTX 3070 (8GB VRAM)