README.md

---
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.