---
language:
- en
- code
pipeline_tag: text-generation
license: apache-2.0
tags:
- coderion
- code
- coding
- reasoning
- small-language-model
- 0.6b
- chronological-reasoning
- high-reasoning
- compact-model
library_name: transformers
datasets:
- nvidia/OpenCodeReasoning
base_model:
- Qwen/Qwen3-0.6B
---

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/YF0kEDYMGJhcM3Lbl2EOD.png" alt="logo" width="250">
</p>


<p align="center"><b>A compact 0.6B coding model built for strong reasoning efficiency.</b></p>

---

**NanoCoder** is a **small 0.6B parameter coding-focused language model** designed for **high and xhigh chronological reasoning** in programming tasks.

It is built to deliver **surprisingly strong structured reasoning and coding performance for its size**, focusing on consistency, logical step progression, and efficient problem solving.

While **NanoCoder is not intended to be a general everyday assistant**, it is a **small but capable specialist model** that performs well within its class and remains **reliable for compact code reasoning workloads**.

---

## Key Characteristics

- **0.6B parameters**
- **Dedicated to code**
- **Optimized for high reasoning intensity**
- **Chronological reasoning style**
- **Strong consistency for a compact model**
- **Designed for efficient performance despite its small size**

---

## Limitations

NanoCoder is a **small specialized model**.

Because of that:

- It may not match larger models on broad real-world assistant tasks
- It is not primarily designed for daily casual use
- It performs best when used for **focused coding and reasoning workloads**
- Its main strength is **efficiency, consistency, and reasoning quality relative to size**