mlx safetensors gguf gemma2 reasoning chain-of-thought CoT Gemma text-generation conversational en dataset:sequelbox/Raiden-DeepSeek-R1 base_model:google/gemma-2-2b-it base_model:quantized:google/gemma-2-2b-it license:mit endpoints_compatible region:us
---
base_model:
- google/gemma-2-2b-it
datasets:
- sequelbox/Raiden-DeepSeek-R1
language:
- en
library_name: mlx
license: mit
pipeline_tag: text-generation
tags:
- gguf
- reasoning
- chain-of-thought
- CoT
- Gemma
---

## Model Summary

This model is a fine-tuned version of **gemma-2-2b-it**, optimized for instruction-following and reasoning tasks. It was trained using **MLX** and **LoRA** on the **sequelbox/Raiden-DeepSeek-R1** dataset, which consists of **62.9k examples** generated by Deepseek R1. The fine-tuning process ran for **600 iterations** to enhance the model’s ability to reason through more complex problems.

## Model Details

-   **Base Model**: gemma-2-2b-it
-   **Fine-tuning Method**: MLX + LoRA
-   **Dataset**: [sequelbox/Raiden-DeepSeek-R1](https://huggingface.co/datasets/sequelbox/Raiden-DeepSeek-R1)
-   **Iterations**: 600

## Capabilities

This model improves upon **gemma-2-2b-it** with additional instruction-following and reasoning capabilities derived from Deepseek R1-generated examples. The model will answer questions with a straight-forward answer for simple questions, and generate long chain-of-thought reasoning text for more complex problems. It is well-suited for:

-   Question answering
-   Reasoning-based tasks
- Coding
- Running on consumer hardware

## Limitations
-   Sometimes chain-of-thought reasoning is not triggered for more complex problems when it probably should be. You can nudge the model if needed by simply asking it to show its thoughts and it will generate think tags and begin reasoning.
-   With harder than average complex reasoning problems, the model can get stuck in long "thinking" thought loops without ever coming to a conclusive answer.