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Model: WithinUsAI/Qwen3-Qrazy.Qoder-0.6B Source: Original Platform
2026-05-14 17:24:36 +08:00
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--- a/README.md
+++ b/README.md
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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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+  "vocab_size": 151936
+}
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