---
license: other
library_name: transformers
base_model:
  - Qwen/Qwen3-0.6B
tags:
  - qwen3
  - code
  - coder
  - reasoning
  - transformers
  - safetensors
  - withinusai
language:
  - en
datasets:
  - microsoft/rStar-Coder
  - open-r1/codeforces-cots
  - nvidia/OpenCodeReasoning
  - patrickfleith/instruction-freak-reasoning
pipeline_tag: text-generation
---

# Qwen3-0.6B-Qrazy-Qoder

**Qwen3-0.6B-Qrazy-Qoder** is a compact coding- and reasoning-oriented language model release from **WithIn Us AI**, built on top of **`Qwen/Qwen3-0.6B`** and packaged as a standard **Transformers** checkpoint in **Safetensors** format.

This model is intended for lightweight coding assistance, reasoning-style prompt workflows, and compact local or hosted inference where a small model footprint is important.

## Model Summary

This model is designed for:

- code generation
- code explanation
- debugging assistance
- reasoning-oriented coding prompts
- implementation planning
- compact instruction following
- lightweight developer assistant workflows

Because this is a **0.6B-class** model, it is best suited for fast, smaller-scope tasks rather than deep long-context reasoning or large multi-file engineering work.

## Base Model

This model is based on:

- **`Qwen/Qwen3-0.6B`**

## Training Data / Dataset Lineage

The current repository README metadata lists the following datasets:

- **`microsoft/rStar-Coder`**
- **`open-r1/codeforces-cots`**
- **`nvidia/OpenCodeReasoning`**
- **`patrickfleith/instruction-freak-reasoning`**

These datasets suggest a blend of:

- code-focused supervision
- competitive-programming-style reasoning
- reasoning-oriented coding data
- instruction-style reasoning prompts

## Intended Use

Recommended use cases include:

- compact coding assistant experiments
- short code generation tasks
- debugging suggestions
- developer Q&A
- reasoning-style technical prompting
- local inference on limited hardware
- lightweight software workflow support

## Suggested Use Cases

This model can be useful for:

- generating short utility functions
- explaining code snippets
- proposing fixes for common bugs
- creating small implementation plans
- answering structured coding questions
- drafting concise technical responses

## Out-of-Scope Use

This model should not be relied on for:

- legal advice
- medical advice
- financial advice
- safety-critical automation
- autonomous production engineering without review
- security-critical code without expert validation

All generated code should be reviewed, tested, and validated before use.

## Repository Contents

The repository currently includes standard Hugging Face model assets such as:

- `README.md`
- `.gitattributes`
- `added_tokens.json`
- `config.json`
- `mergekit_config.yml`
- `merges.txt`
- `model.safetensors`
- `special_tokens_map.json`
- `tokenizer.json`
- `tokenizer_config.json`

## Prompting Guidance

This model generally works best when prompts are:

- direct
- scoped to one task
- explicit about the language or framework
- clear about whether code, explanation, or both are wanted
- structured when reasoning is needed

### Example prompt styles

**Code generation**
> Write a Python function that removes duplicate records from a JSON list using the `id` field.

**Debugging**
> Explain why this JavaScript function returns `undefined` and provide a corrected version.

**Reasoning-oriented coding**
> Compare two approaches for caching API responses in Python and recommend one.

**Implementation planning**
> Create a step-by-step plan for building a small Flask API with authentication and tests.

## Strengths

This model may be especially useful for:

- compact coding workflows
- lightweight reasoning prompts
- low-resource deployments
- quick iteration
- structured developer assistance
- small local inference setups

## Limitations

Like other compact language models, this model may:

- hallucinate APIs or library behavior
- generate incomplete or incorrect code
- struggle with long-context tasks
- make reasoning mistakes on harder prompts
- require prompt iteration for best results
- underperform larger coding models on advanced engineering tasks

Human review is strongly recommended.

## Attribution

**WithIn Us AI** is the publisher of this model release.

Credit for upstream assets remains with their original creators, including:

- **Qwen** for **`Qwen/Qwen3-0.6B`**
- **Microsoft** for **`microsoft/rStar-Coder`**
- the creators of **`open-r1/codeforces-cots`**
- **NVIDIA** for **`nvidia/OpenCodeReasoning`**
- **patrickfleith** for **`patrickfleith/instruction-freak-reasoning`**

## License

This draft uses:

- `license: other`

If you maintain this repo, replace this with the exact license terms you want displayed and ensure they align with any upstream licensing requirements.

## Acknowledgments

Thanks to:

- **WithIn Us AI**
- **Qwen**
- **Microsoft**
- **NVIDIA**
- the dataset creators listed above
- the Hugging Face ecosystem
- the broader open-source AI community

## Disclaimer

This model may produce inaccurate, insecure, incomplete, or misleading outputs. All important generations, especially code and technical guidance, should be reviewed and tested before real-world use.