---
language:
- en
pipeline_tag: text-generation
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- unsloth
- qwen
- text-generation
- code
---

# Model Card for GEAR-2-500m-Identity

## Model Details

### Model Description

GEAR-2-500m-Identity is a lightweight Transformer LLM with approximately 0.5 billion parameters, fine-tuned on the Qwen2.5 architecture using Unsloth. It is designed to run extremely fast on local machines (CPU/Edge) with minimal memory usage. The model embodies the persona of **Gear**, an intelligent assistant created by **HeavensHack**.

It is capable of code generation (Python) and general chat. While efficient, it is a small model and may struggle with complex reasoning compared to larger parameters.

- **Developed by:** HeavensHack
- **Model type:** Qwen2 For Causal LM
- **Language(s) (NLP):** English, Python (Code) , **(New)** *Russian*
- **License:** Apache 2.0
- **Finetuned from model:** Qwen/Qwen2.5-0.5B-Instruct

## Uses

### Direct Use

- Fast local chat assistant
- Python code generation and debugging

### Out-of-Scope Use

- Complex mathematical reasoning
- High-stakes decision making
- Long-context analysis requiring high accuracy

## Bias, Risks, and Limitations

- **Hallucinations:** Due to the 0.5B parameter size, it may generate plausible but incorrect information.
- **Identity:** The model is strictly fine-tuned to identify itself as "Gear" by HeavensHack.
- **Inconsistency:** Behavior might be variable in long conversations.

### Recommendations

- Use for educational purposes, hobby projects, or low-resource environments.
- Verify any code generated before running it in production.

## How to Get Started

- Load the model using Unsloth or standard Hugging Face transformers.
- Optimized for local inference.

## Training Details

- **Training Data:** Custom identity dataset (HeavensHack), Alpaca (English), and Python Code instructions.
- **Training Procedure:** Fine-tuned using Unsloth (LoRA) for efficiency.
- **Training Regime:** Mixed precision (BF16/FP16).

## Evaluation

- Validated for identity retention and basic coding tasks.
- Not benchmarked for enterprise production use.

## Environmental Impact

- Extremely low compute cost during training due to Unsloth optimization.

## Model Card Contact

- **Author:** HeavensHackDev

# But...

  - **At first, only the GGUF file will be available. The rest will follow later.**