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:
Training Data / Dataset Lineage
The current repository README metadata lists the following datasets:
microsoft/rStar-Coderopen-r1/codeforces-cotsnvidia/OpenCodeReasoningpatrickfleith/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.gitattributesadded_tokens.jsonconfig.jsonmergekit_config.ymlmerges.txtmodel.safetensorsspecial_tokens_map.jsontokenizer.jsontokenizer_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:
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.
