Create README.md

README.md

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+---
+license: other
+library_name: llama.cpp
+tags:
+  - gguf
+  - imatrix
+  - qwen3
+  - code
+  - coder
+  - text-generation
+  - local-inference
+  - withinusai
+language:
+  - en
+model_type: gguf
+inference: false
+---
+
+# Qwen3-0.6B-Qrazy-Qoder-i1-GGUF
+
+**Qwen3-0.6B-Qrazy-Qoder-i1-GGUF** is a compact GGUF release from **WithIn Us AI**, designed for local inference and lightweight coding-oriented text generation.
+
+This repository packages a **0.6B-parameter Qwen3-family model** in GGUF format for efficient use with **llama.cpp** and compatible local inference runtimes.
+
+## Model Summary
+
+This model is intended for:
+
+- lightweight local coding assistance
+- code drafting and code completion
+- short prompt engineering workflows
+- offline experimentation
+- compact reasoning-style assistant tasks
+- low-resource deployments
+
+Because this is a **0.6B-class** model, it is best used for small, fast, practical tasks rather than deep multi-step reasoning or large-scale production code generation.
+
+## Repository Contents
+
+This repository currently includes the following GGUF files:
+
+- `Qwen3-0.6B-Qrazy-Qoder.i1-Q4_K_M.gguf`
+- `Qwen3-0.6B-Qrazy-Qoder.i1-Q5_K_M.gguf`
+- `Qwen3-0.6B-Qrazy-Qoder.i1-Q6_K.gguf`
+
+## Architecture
+
+The repository metadata identifies the architecture as:
+
+- **qwen3**
+
+## Quantization Variants
+
+### Q4_K_M
+A smaller quantization for lower memory use and faster inference on limited hardware.
+
+### Q5_K_M
+A balanced option for users who want a stronger quality-to-size tradeoff.
+
+### Q6_K
+A heavier quantization with potentially better output quality when memory budget allows.
+
+## Intended Use
+
+Recommended use cases include:
+
+- local coding assistant experiments
+- offline chatbot or helper tools
+- code explanation and refactoring drafts
+- compact prompt-response applications
+- embedded or low-resource AI workflows
+- rapid testing of small coding models
+
+## Suggested Use Cases
+
+This model can be useful for:
+
+- generating short utility functions
+- explaining simple code snippets
+- drafting boilerplate
+- rewriting small functions for readability
+- proposing debugging ideas
+- producing structured text outputs for developer workflows
+
+## Out-of-Scope Use
+
+This model should not be relied on for:
+
+- legal advice
+- medical advice
+- financial advice
+- safety-critical automation
+- unsupervised production code generation
+- security-sensitive engineering without human review
+
+All generated code should be reviewed and tested before deployment.
+
+## Performance Expectations
+
+As a compact **0.6B** model, this release prioritizes:
+
+- portability
+- low memory use
+- quick local inference
+- simple coding workflows
+
+It may struggle with:
+
+- long-context tasks
+- highly complex debugging
+- strict factual accuracy
+- advanced architectural planning
+- deep multi-step reasoning
+- large multi-file codebase understanding
+
+## Prompting Tips
+
+For best results, use prompts that are:
+
+- specific
+- direct
+- limited in scope
+- explicit about the language
+- clear about the desired output format
+
+### Example prompt styles
+
+**Code generation**
+> Write a Python function that removes duplicate email addresses from a CSV file and saves the cleaned output.
+
+**Debugging**
+> Explain why this JavaScript function throws `undefined` and provide a corrected version.
+
+**Refactoring**
+> Refactor this Python function to improve readability and add error handling.
+
+## Runtime Notes
+
+This model is distributed in **GGUF** format and is intended for use with runtimes that support GGUF, such as:
+
+- llama.cpp
+- compatible local desktop frontends
+- supported lightweight inference backends
+
+Choose your quantization based on your hardware:
+
+- use **Q4_K_M** for smaller RAM usage
+- use **Q5_K_M** for a quality / efficiency balance
+- use **Q6_K** when you want a stronger output-quality tilt and can afford the extra memory
+
+## Limitations
+
+Like other small language models, this model may:
+
+- hallucinate APIs or library behavior
+- generate incorrect or incomplete code
+- lose instruction fidelity on longer prompts
+- produce repetitive responses
+- make reasoning mistakes
+- require prompt iteration to get clean outputs
+
+Human review is strongly recommended.
+
+## Creator
+
+**WithIn Us AI** is the creator of this model release, including the packaging, naming, quantized GGUF distribution, and any fine-tuning / merging process associated with this release.
+
+## License
+
+This model card uses:
+
+- `license: other`
+
+You can replace this with your exact **WithIn Us AI custom license terms**.
+
+If this release is derived from upstream models, merged checkpoints, or third-party datasets, include:
+
+- attribution to the original base model creators
+- attribution to any third-party datasets used
+- a clear statement that WithIn Us AI claims authorship of the fine-tuning / merging / packaging process, not ownership of third-party source materials unless applicable
+
+## Acknowledgments
+
+Thanks to:
+
+- the original Qwen creators
+- the GGUF and llama.cpp ecosystem
+- Hugging Face hosting infrastructure
+- the broader open-source AI community
+
+## Disclaimer
+
+This model may produce inaccurate, biased, insecure, or incomplete outputs.  
+Use responsibly, and verify all important results before real-world use.