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--- |
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tags: |
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- text-generation |
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- masked-language-modeling |
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- browser-compatible |
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- micro-model |
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- social-media |
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- linkspreed |
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- Web4 |
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datasets: |
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- custom |
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metrics: |
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- custom |
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library_name: transformers |
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base_model: google/gemma-270m |
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--- |
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# LS-W4-270M-Micro-T1 |
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## Model Description |
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**LS-W4-270M-Micro-T1** is the first model in the **Web4 Localized Services (W4-LS)** series, specifically designed for highly efficient, on-device text generation. As a **Micro Language Model (Micro-LM)**, it features a compact architecture with a total of **$2 \times 270$ million parameters**. |
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This model is a **Masked Language Model (MLM)** specialized in generating **social media captions**. It prioritizes inference speed and minimal resource usage, making it ideal for **client-side execution**. |
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### Key Features 🚀 |
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* **Base Architecture:** Built on top of **Gemma 3 270M**. |
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* **Micro-LM Architecture:** Optimized for low-latency performance on consumer devices. |
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* **Social Media Specialization:** Trained to generate engaging and contextually relevant social media captions. |
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* **Serverless Operation:** A core innovation of this model is its ability to run **entirely locally within a web browser or on a client device** without requiring a server. This ensures full **privacy** and **offline functionality**. |
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## How to Use: Serverless Deployment |
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The model is designed exclusively for **serverless environments** and **cannot** be executed using traditional Hugging Face inference endpoints. |
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### Client-Side/On-Device Deployment Files |
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To run this model locally in a browser or on a device, the necessary client-side deployment files are required. |
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**The required `.task` and `.tflite` files for local deployment can be downloaded at:** |
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[https://ai.web4.one](https://ai.web4.one) |
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## Model Details |
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**Model Name:** LS-W4-270M-Micro-T1 |
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**Model Type:** Masked Language Model (MLM) |
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**Parameters:** 540 Million (2×270 Million) |
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**Base Model:** Gemma 3 270M |
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**Primary Task:** Social Media Caption Generation (Serverless/Local Inference) |
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**License:** Same license as the base model **Gemma 3 270M** |
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## Training Details 🛠️ |
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The model was fine-tuned specifically for the task of social media caption generation. |
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**Training Data Size:** Over 50,000 datasets (examples/entries) were used for fine-tuning. |
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**Training Hardware:** Fine-tuning was performed on a **T4 GPU with 12 GB of RAM**. |
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