| --- |
| 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](https://huggingface.co/WithinUsAI/flanT5-MoE-7X0.1B-PythonGOD-25k/tree/main) |
| |
| ## 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. |
