61 lines
1.7 KiB
Markdown
61 lines
1.7 KiB
Markdown
|
---
|
||
|
|
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**
|