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Model: WithinUsAI/Qwen3-Qrazy.Qoder-0.6B
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---
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.

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