README.md

---
license: other
library_name: transformers
base_model:
  - gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k
tags:
  - t5
  - text2text-generation
  - generated_from_trainer
  - code
  - agentic-ai
  - instruction-following
  - withinusai
language:
  - en
datasets:
  - gss1147/Python_GOD_Coder_25k
  - WithinUsAI/Got_Agentic_AI_5k
model-index:
  - name: flanT5-MoE-7X0.1B-PythonGOD-AgenticAI
    results: []
---

# flanT5-MoE-7X0.1B-PythonGOD-AgenticAI

**flanT5-MoE-7X0.1B-PythonGOD-AgenticAI** is a text-to-text generation model from **WithIn Us AI**, built as a fine-tuned derivative of **`gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k`** and further trained for coding-oriented and agentic-style instruction following.

This model is intended for lightweight local or hosted inference workflows where a compact instruction-tuned model is useful for structured responses, code help, implementation planning, and tool-oriented prompting.

## Model Summary

This model is designed for:

- code-oriented instruction following
- lightweight agentic prompting
- implementation planning
- coding assistance
- structured text generation
- compact text-to-text tasks

Because this model is built in the **Flan-T5 / T5 text-to-text style**, it is best prompted with clear task instructions and expected outputs rather than open-ended chat-only prompting.

## Base Model

This model is a fine-tuned version of:

- **`gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k`**

## Training Data

The current repository metadata identifies the following datasets in the model lineage:

- **`gss1147/Python_GOD_Coder_25k`**
- **`WithinUsAI/Got_Agentic_AI_5k`**

This model card reflects the currently visible metadata on the Hugging Face repository.

## Intended Use

Recommended use cases include:

- Python and general coding help
- instruction-based code generation
- implementation planning
- structured assistant responses
- compact agentic AI experiments
- transformation tasks such as rewriting, summarizing, and reformatting technical text

## Suggested Use Cases

This model can be useful for:

- generating small code snippets
- rewriting code instructions into actionable steps
- producing structured implementation plans
- answering coding questions in text-to-text format
- converting prompts into concise development outputs
- supporting lightweight agent-style task decomposition

## Out-of-Scope Use

This model should not be relied on for:

- legal advice
- medical advice
- financial advice
- fully autonomous high-stakes decision making
- security-critical code generation without human review
- production deployment without evaluation and testing

All generated code and technical guidance should be reviewed by a human before real-world use.

## Architecture and Format

This repository is currently tagged as:

- **`t5`**
- **`text2text-generation`**

The model is distributed as a standard Hugging Face Transformers checkpoint with files including:

- `config.json`
- `generation_config.json`
- `model.safetensors`
- `tokenizer.json`
- `tokenizer_config.json`
- `training_args.bin`

## Prompting Guidance

This model is best used with direct instruction prompts. Clear task framing tends to work better than vague prompts.

### Example prompt styles

**Code generation**
> Write a Python function that loads a JSON file, validates required keys, and returns cleaned records.

**Implementation planning**
> Create a step-by-step implementation plan for building a Flask API with authentication and logging.

**Debugging help**
> Explain why this Python function fails on missing keys and rewrite it with safe error handling.

**Agentic task framing**
> Break this software request into ordered implementation steps, dependencies, and testing tasks.

## Strengths

This model may be especially useful for:

- compact inference footprints
- instruction-following behavior
- coding-oriented prompt tasks
- text transformation workflows
- lightweight task decomposition
- structured output generation

## Limitations

Like other compact language models, this model may:

- hallucinate APIs or implementation details
- produce incomplete or overly simplified code
- lose accuracy on long or complex prompts
- make reasoning mistakes on deep multi-step tasks
- require prompt iteration for best results
- underperform larger models on advanced planning or debugging

Human review is strongly recommended.

## Training and Attribution Notes

WithIn Us AI is the creator of this model release and its packaging, naming, and fine-tuning presentation.

This card does **not** claim ownership over third-party or upstream assets unless explicitly stated by their original creators. Credit remains with the creators of the upstream base model and any datasets used in training.

## License

This model card uses:

- `license: other`

Use the repository `LICENSE` file or project-specific license text to define the exact redistribution and usage terms.

## Acknowledgments

Thanks to:

- **WithIn Us AI**
- the creators of **`gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k`**
- the dataset creators behind **`gss1147/Python_GOD_Coder_25k`** and **`WithinUsAI/Got_Agentic_AI_5k`**
- the Hugging Face ecosystem
- the broader open-source ML community

## Disclaimer

This model may produce inaccurate, incomplete, insecure, or biased outputs. All generations, especially code and implementation guidance, should be reviewed and tested before real-world use.