README.md

metadata

license: other
library_name: transformers
base_model:
  - gss1147/flanT5-MoE-7X0.1B
tags:
  - t5
  - Google
  - PythonGODCoder25x
  - code
  - coding-assistant
  - text2text-generation
  - instruction-following
  - withinusai
language:
  - en
datasets:
  - gss1147/Python_GOD_Coder_25k
  - deepmind/code_contests
  - djaym7/wiki_dialog
pipeline_tag: text2text-generation

flanT5-MoE-7X0.1B-PythonGOD-25k

flanT5-MoE-7X0.1B-PythonGOD-25k is a compact text-to-text generation model from WithIn Us AI, built on top of gss1147/flanT5-MoE-7X0.1B and positioned for coding-oriented instruction following, technical prompting, and lightweight structured generation.

This model is best suited for users who want a small T5-style checkpoint for code-help tasks, prompt-to-output transformations, implementation planning, and concise assistant workflows.

Model Summary

This model is designed for:

• code-oriented instruction following
• Python-focused prompt tasks
• structured text-to-text generation
• compact implementation assistance
• lightweight coding workflows
• technical transformation tasks

Because this model follows the T5 / Flan-T5 text-to-text format, it generally performs best when prompts are written as direct tasks rather than as vague open-ended chat.

Base Model

This model is based on:

• gss1147/flanT5-MoE-7X0.1B

Training Data

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

• gss1147/Python_GOD_Coder_25k
• deepmind/code_contests
• djaym7/wiki_dialog

These sources suggest a blend of coding-focused supervision, contest-style programming content, and conversational or dialogue-style instruction material.

Intended Use

This model is intended for:

• code generation prompts
• coding assistant prototypes
• instruction-based code rewriting
• implementation planning
• compact local or hosted inference
• structured development-task responses

Recommended Use Cases

This model can be used for:

• generating short Python functions
• rewriting code into cleaner or more readable form
• explaining snippets of code
• producing small implementation plans
• answering coding prompts in a concise format
• transforming developer requests into structured outputs

Out-of-Scope Use

This model should not be relied on for:

• legal advice
• medical advice
• financial advice
• autonomous production code deployment
• security-critical code generation without review
• high-stakes decisions without human verification

All generated code should be reviewed, tested, and validated before use.

Model Format

This repository currently includes standard Hugging Face model artifacts such as:

• config.json
• generation_config.json
• model.safetensors
• tokenizer.json
• tokenizer_config.json

The model is hosted as a Transformers checkpoint and is suitable for standard transformers inference workflows. oai_citation:1‡Hugging Face

Prompting Guidance

This model works best with clear, direct instructions.

Example prompt styles

Code generation

Write a Python function that loads a JSON file, removes duplicate records by email, and saves the cleaned result.

Explanation

Explain what this Python function does and identify any bugs or edge cases.

Refactoring

Refactor this code for readability and add error handling.

Planning

Create a step-by-step implementation plan for a simple Flask API with login and logging.

Strengths

This model may be especially useful for:

• compact inference footprints
• text-to-text coding prompts
• structured responses
• lightweight implementation help
• fast experimentation
• small-model workflows

Limitations

Like other compact language models, this model may:

• hallucinate APIs or code details
• generate incomplete or incorrect code
• struggle with long or deeply complex tasks
• lose precision on multi-step reasoning
• require prompt iteration for best results
• underperform larger models on advanced debugging and architecture work

Human review is strongly recommended.

Attribution

WithIn Us AI is the creator of this release, including the model packaging, presentation, and project identity.

Credit for upstream assets remains with their original creators, including:

• the creators of gss1147/flanT5-MoE-7X0.1B
• the creators of gss1147/Python_GOD_Coder_25k
• DeepMind for deepmind/code_contests
• the creator of djaym7/wiki_dialog

License

This model card uses:

• license: other

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

Acknowledgments

Thanks to:

• WithIn Us AI
• the upstream creators of the base model
• the dataset creators listed above
• the Hugging Face ecosystem
• the open-source ML community

Disclaimer

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