Update README.md

README.md

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 ---
-datasets:
-- WithinUsAI/Python_GOD_Coder_50k
-- reedmayhew/gemini-3.1-pro-2048-reasoning-1100x
-- m-a-p/Code-Feedback
-- crownelius/Opus-4.6-Reasoning-2100x-formatted
-- crownelius/Opus4.6-No-Reasoning-260x
-- crownelius/Creative_Writing_Multiturn_Enhanced
-- HuggingFaceH4/llava-instruct-mix-vsft
-- Roman1111111/gemini-3-pro-10000x-hard-high-reasoning
+license: other
 base_model:
-- Qwen/Qwen3.5-4B
----
+  - Qwen/Qwen3.5-4B
+library_name: llama.cpp
+tags:
+  - gguf
+  - qwen
+  - qwen3.5
+  - code
+  - coder
+  - conversational
+  - text-generation
+  - withinusai
+language:
+  - en
+datasets:
+  - WithinUsAI/Python_GOD_Coder_50k
+  - reedmayhew/gemini-3.1-pro-2048-reasoning-1100x
+  - m-a-p/Code-Feedback
+  - crownelius/Opus-4.6-Reasoning-2100x-formatted
+  - crownelius/Opus4.6-No-Reasoning-260x
+  - crownelius/Creative_Writing_Multiturn_Enhanced
+  - HuggingFaceH4/llava-instruct-mix-vsft
+  - Roman1111111/gemini-3-pro-10000x-hard-high-reasoning
+model_type: gguf
+inference: false
+---
+
+# WithIn-Us-Coder-4B.gguf
+
+**WithIn-Us-Coder-4B.gguf** is a GGUF release from **WithIn Us AI**, built for local inference and coding-focused assistant use cases. It is based on **Qwen/Qwen3.5-4B** and distributed in quantized GGUF formats for efficient deployment in llama.cpp-compatible runtimes.
+
+## Model Summary
+
+This model is intended as a coding-oriented conversational assistant with emphasis on:
+
+- code generation
+- code reasoning
+- implementation planning
+- debugging assistance
+- instruction following
+- general assistant-style chat for development workflows
+
+This repository currently provides the following GGUF variants:
+
+- `WithIn-Us-Coder-4B.Q4_K_M.gguf`
+- `WithIn-Us-Coder-4B.Q5_K_M.gguf`
+
+## Creator
+
+**WithIn Us AI** is the creator of this model release, including the model packaging, fine-tuning / merging concept, process, naming, and GGUF distribution.
+
+## Base Model
+
+This model is based on:
+
+- **Qwen/Qwen3.5-4B**
+
+Credit and appreciation go to the original creators of the base LLM architecture and weights.
+
+## Training Data
+
+The current repository metadata lists the following datasets as part of the model’s training / fine-tuning lineage:
+
+- `WithinUsAI/Python_GOD_Coder_50k`
+- `reedmayhew/gemini-3.1-pro-2048-reasoning-1100x`
+- `m-a-p/Code-Feedback`
+- `crownelius/Opus-4.6-Reasoning-2100x-formatted`
+- `crownelius/Opus4.6-No-Reasoning-260x`
+- `crownelius/Creative_Writing_Multiturn_Enhanced`
+- `HuggingFaceH4/llava-instruct-mix-vsft`
+- `Roman1111111/gemini-3-pro-10000x-hard-high-reasoning`
+
+**Attribution note:**  
+WithIn Us AI does not claim ownership over third-party base models or third-party datasets. Full credit, thanks, and attribution belong to the original model and dataset creators.
+
+## Intended Use
+
+This model is intended for:
+
+- local coding assistants
+- offline development help
+- code explanation
+- bug-fixing support
+- prompt-based code generation
+- experimentation in llama.cpp and GGUF-compatible environments
+
+### Suggested Use Cases
+
+- generating Python, JavaScript, C++, and other programming language snippets
+- explaining code blocks
+- rewriting or improving functions
+- brainstorming implementation strategies
+- creating scaffolding and prototypes
+- assisting with debugging and refactoring
+
+## Out-of-Scope Use
+
+This model is not guaranteed to be reliable for:
+
+- high-stakes legal advice
+- medical advice
+- financial decision-making
+- autonomous execution without review
+- security-critical production decisions without human verification
+
+Users should always validate generated code before deployment.
+
+## Quantization Formats
+
+This repository currently includes:
+
+- **Q4_K_M** for smaller memory footprint and faster local inference
+- **Q5_K_M** for improved quality while remaining efficient
+
+Choose the quant level based on your hardware budget and quality needs.
+
+## Prompting Notes
+
+As a coding-focused conversational model, best results usually come from prompts that are:
+
+- specific
+- structured
+- explicit about language, framework, or goal
+- clear about desired output format
+
+Example prompt style:
+
+> Write a Python function that parses a CSV file, removes duplicate rows by email, and saves the cleaned result. Include error handling and comments.
+
+## Limitations
+
+Like other language models, this model may:
+
+- hallucinate APIs or library behavior
+- generate insecure or inefficient code
+- make reasoning mistakes
+- produce outdated patterns
+- require prompt iteration for best results
+
+Human review is strongly recommended, especially for production code.
+
+## License
+
+This repository uses a **custom WithIn Us AI license approach**.
+
+- The base model may be subject to its original upstream license and terms.
+- Third-party datasets remain the property of their respective creators / licensors.
+- WithIn Us AI claims authorship of the fine-tuning / merging concept, process, packaging, naming, and release structure for this model distribution.
+- This repository does **not** claim ownership over third-party datasets or the underlying upstream base model.
+
+You can include a `LICENSE` file in this repository with the exact custom terms you want enforced.
+
+## Acknowledgments
+
+Special thanks to:
+
+- **Qwen** for the base model
+- all third-party dataset creators listed above
+- the open-source GGUF / llama.cpp ecosystem
+- the broader Hugging Face community
+
+## Files
+
+Current repository files include:
+
+- `WithIn-Us-Coder-4B.Q4_K_M.gguf`
+- `WithIn-Us-Coder-4B.Q5_K_M.gguf`
+
+## Disclaimer
+
+This model may generate incorrect, biased, insecure, or incomplete outputs.  
+Use responsibly, validate important results, and review all generated code before real-world use.