GPT5.1-High.Reasoning.Codex-0.4B-GGUF

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.