| --- |
| 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. |
