Create README.md

README.md

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+---
+license: other
+library_name: llama.cpp
+tags:
+  - gguf
+  - gpt2
+  - code
+  - coder
+  - reasoning
+  - text-generation
+  - withinusai
+language:
+  - en
+model_type: gguf
+inference: false
+---
+
+# GPT5.1-high-reasoning-codex-0.4B-GGUF
+
+**GPT5.1-high-reasoning-codex-0.4B-GGUF** is a compact GGUF language model release from **WithIn Us AI**, intended for local inference and lightweight coding or reasoning-oriented experiments.
+
+This repository provides quantized GGUF builds for efficient use with **llama.cpp** and compatible runtimes.
+
+## Model Summary
+
+This model is designed for:
+
+- lightweight local inference
+- coding and prompt-based development assistance
+- compact reasoning-style experiments
+- offline chat and text generation workflows
+- small-footprint deployments
+
+Because this is a **0.4B** parameter class model, it is best suited for fast iteration, simple coding tasks, prompt experiments, structured text generation, and lightweight assistant workflows rather than heavy long-context reasoning or complex production-grade coding autonomy.
+
+## Repository Contents
+
+This repository currently includes the following files:
+
+- `GPT5.1-high-reasoning-codex-0.4B.Q4_K_M.gguf`
+- `GPT5.1-high-reasoning-codex-0.4B.Q5_K_M.gguf`
+- `GPT5.1-high-reasoning-codex-0.4B.f16.gguf`
+
+## Quantization Variants
+
+### Q4_K_M
+A smaller and more memory-efficient quantization for lower RAM usage and faster local inference.
+
+### Q5_K_M
+A slightly larger quantization that may provide somewhat better output quality while remaining efficient.
+
+### F16
+A higher-precision GGUF variant intended for users who want the least quantization loss and have more memory available.
+
+## Architecture
+
+The repository metadata currently identifies the architecture as:
+
+- **gpt2**
+
+## Intended Use
+
+Recommended use cases include:
+
+- local coding assistant experiments
+- toy and lightweight software-help workflows
+- code completion and code drafting
+- debugging ideas and implementation suggestions
+- instruction-following tests
+- prompt engineering experiments
+- low-resource local deployments
+
+## Out-of-Scope Use
+
+This model should not be relied on for:
+
+- legal advice
+- medical advice
+- financial advice
+- safety-critical automation
+- production code generation without review
+- security-sensitive decisions without human verification
+
+All generated code should be reviewed, tested, and validated before use.
+
+## Performance Expectations
+
+As a compact **0.4B** model, this release trades raw capability for speed, portability, and lower hardware requirements. It may perform well for:
+
+- short code snippets
+- compact prompts
+- structured assistant replies
+- lightweight reasoning-style tasks
+
+It may struggle with:
+
+- long and complex codebases
+- deep multi-step reasoning
+- strict factual reliability
+- advanced tool orchestration
+- heavy instruction retention over long prompts
+
+## Prompting Tips
+
+For best results, use prompts that are:
+
+- specific
+- short to medium length
+- explicit about the desired language or format
+- clear about constraints
+- direct about whether you want code, explanation, or both
+
+### Example prompts
+
+**Code generation**
+> Write a Python function that reads a JSON file, validates required fields, and returns a cleaned list of records.
+
+**Refactoring**
+> Refactor this JavaScript function to be more readable and add basic error handling.
+
+**Debugging**
+> Explain why this Python code raises a KeyError and show a corrected version.
+
+## Hardware and Runtime Notes
+
+This model is packaged in **GGUF** format, which is suitable for **llama.cpp**-style local inference stacks and related frontends / runtimes that support GGUF models.
+
+Typical choices:
+
+- use **Q4_K_M** for smaller memory usage
+- use **Q5_K_M** for a quality / size balance
+- use **F16** when memory allows and you want higher precision
+
+## Limitations
+
+Like other small language models, this model may:
+
+- hallucinate APIs, functions, or package behavior
+- generate incorrect code
+- produce insecure code patterns
+- make reasoning mistakes
+- lose instruction fidelity on longer prompts
+- require prompt retries for acceptable output quality
+
+Human oversight is strongly recommended.
+
+## Training / Lineage
+
+This repository is presented as a **WithIn Us AI** model release and GGUF packaging distribution.
+
+If you want, this section can be expanded later with:
+
+- base model lineage
+- fine-tuning details
+- merge methodology
+- dataset attribution
+- training objective
+- chat template recommendations
+
+## License
+
+This repository currently uses a custom / non-standard license field approach in this model card draft:
+
+- `license: other`
+
+You can replace this section with your exact **WithIn Us AI custom license terms**. If this model is derived from upstream weights or datasets, include:
+
+- attribution to the original base model creators
+- attribution to any third-party datasets used
+- 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 open-source local inference ecosystem
+- GGUF and llama.cpp tooling contributors
+- the broader Hugging Face community
+- all upstream creators whose work may have contributed to the model’s lineage
+
+## Disclaimer
+
+This model may produce inaccurate, biased, insecure, or incomplete outputs.  
+Use responsibly, and verify important results before real-world use.