| --- |
| 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. |
