flanT5-MoE-7X0.1B
flanT5-MoE-7X0.1B is a compact Mixture of Experts (MoE) text-to-text generation model published by WithIn Us AI. It is built from google/flan-t5-small and presented as a 7-expert MoE model intended for lightweight instruction-following, coding-oriented prompting, reasoning-style tasks, summarization, and dialogue-style generation.
This model is designed for users who want a small and efficient T5-family checkpoint for experimentation, local inference, and structured prompt-to-output workflows.
Model Summary
This model is intended for:
- instruction-following tasks
- lightweight coding and programming prompts
- reasoning-style text generation
- summarization and dialogue tasks
- compact text-to-text workflows
- experimentation with small Mixture of Experts architectures
Because this model follows the T5 / Flan-T5 text-to-text format, it generally works best when prompted with clear task instructions rather than open-ended casual chat.
Base Model
This model is based on:
Model Architecture
According to the current repository metadata and README, this model is a 7-expert Mixture of Experts merge built from repeated google/flan-t5-small experts with different prompt affinities. The README describes the expert routing themes as:
- instruction / task
- reasoning / logic
- creative / writing
- code / programming
- science / facts
- math / calculation
- summary / dialogue
The current model page also lists the model as a T5, Mixture of Experts, Merge, mergekit, and lazymergekit project.
Training Data
The current repository metadata lists the following datasets in the model lineage:
deepmind/code_contestssvakulenk0/qreccdjaym7/wiki_dialog
These suggest a blend of coding, conversational retrieval, and dialogue-oriented supervision.
Intended Use
This model is intended for:
- compact instruction-based generation
- small coding assistant experiments
- educational prompt-response systems
- summarization and reformulation
- dialogue-style text transformation
- lightweight research into MoE behavior
Recommended Use Cases
This model can be useful for:
- generating short code-oriented responses
- answering structured prompts
- summarizing small passages
- rewriting text into cleaner or more concise form
- handling lightweight reasoning prompts
- experimenting with expert-routed small models
Out-of-Scope Use
This model should not be relied on for:
- legal advice
- medical advice
- financial advice
- safety-critical automation
- unsupervised production deployment
- high-stakes factual systems without human verification
All generated outputs should be reviewed before real-world use.
Repository Contents
The repository currently includes standard Transformers artifacts such as:
config.jsongeneration_config.jsonmodel.safetensorstokenizer.jsontokenizer_config.json
Prompting Guidance
This model works best with direct task instructions.
Example prompt styles
Instruction following
Explain recursion in simple terms for a beginner.
Coding
Write a short Python function that removes duplicate values from a list while preserving order.
Summarization
Summarize the following paragraph in three bullet points.
Dialogue / QA
Rewrite this answer so it sounds clearer and more helpful.
Strengths
This model may be especially useful for:
- compact inference
- structured text-to-text tasks
- small-scale coding prompts
- efficient summarization
- lightweight experimentation with MoE routing
- educational and prototype workflows
Limitations
Like other compact language models, this model may:
- hallucinate facts or APIs
- produce incomplete code
- struggle with long-context tasks
- simplify complex reasoning too aggressively
- require prompt iteration for best results
- underperform larger models on advanced technical work
Human review is recommended.
Attribution
WithIn Us AI is the publisher of this model release.
Credit for upstream assets remains with their original creators, including:
- Google for
google/flan-t5-small
- DeepMind for
deepmind/code_contests
- the creators of
svakulenk0/qrecc
- the creators of
djaym7/wiki_dialog
License
This repository is currently marked:
Use of this model should remain consistent with the repository license and any applicable upstream terms.
Acknowledgments
Thanks to:
- WithIn Us AI
- Google
- DeepMind
- the dataset creators listed above
- the Hugging Face ecosystem
- the open-source merge and MoE tooling community
Disclaimer
This model may produce inaccurate, incomplete, or biased outputs. Review important outputs carefully before real-world use.
