🤖 Web4/LS-W4-270M-Micro-Hashtags Model Card

💻 Model Details

• Model Name: Web4/LS-W4-270M-Micro-Hashtags
• Model Type: Micro Language Model (MLM)
• Model Size: 270 Million parameters (270M)
• Developer: Web4
• Intended Use: On-device, offline, and browser-based hashtag generation from user text input
• License: MIT

📄 Model Description

The LS-W4-270M-Micro-Hashtags is a compact, high-efficiency 270M parameter Micro Language Model (MLM) developed by Web4.
It is designed specifically for environments with limited computational resources, such as mobile devices, desktop apps, and browsers — with no server required.

Its primary function is to generate relevant, trending, and semantically meaningful hashtags from input text in real time.
It runs fully offline, supports WebAssembly/WebGPU, and enables privacy-preserving, decentralized AI applications.

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📊 Training Data and Procedure

• Training Dataset Size: Over 1 million diverse datasets
• Data Sources: Social media posts, online articles, and real-world text snippets tagged with hashtags
• Objective: Masked Language Modeling (MLM) + fine-tuning for hashtag prediction
• Performance Optimizations: Designed for fast inference, low latency, and minimal memory footprint

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⚙️ Technical Specifications

Specification	Value
Parameters	270M
Model Format	ONNX, Web4 Custom Binary, WebAssembly
Operating Environment	On-device (mobile/desktop), Browser (WASM/WebGPU)
Server Requirement	None (fully offline)
Inference Speed	Real-time on consumer hardware
Primary Function	Hashtag generation from freeform text