Update README.md

README.md

@@ -1,19 +1,177 @@
 ---
+license: other
 library_name: transformers
-base_model: gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k
+base_model:
+  - gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k
 tags:
-- generated_from_trainer
-model-index:
-- name: flanT5-MoE-7X0.1B-PythonGOD-25k-finetuned-GotAgenticAI
-  results: []
+  - 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
+  - gss1147/Python_GOD_Coder_25k
+  - WithinUsAI/Got_Agentic_AI_5k
+model-index:
+  - name: flanT5-MoE-7X0.1B-PythonGOD-AgenticAI
+    results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# 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
 
-# flanT5-MoE-7X0.1B-PythonGOD-25k-finetuned-GotAgenticAI
+## Disclaimer
 
-This model is a fine-tuned version of [gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k](https://huggingface.co/gss1147/flanT5-MoE-7X0.1B-PythonGOD-25k) on an WithinUsAI/Got_Agentic_AI_5k dataset.
+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.