Web4/LS-W4-Mini-SM-Post-Relevance-270M-it-GGUF

Model Description

This model is a fine-tuned version of Google's gemma-3-270m-it specifically adapted to generate relevant social media posts in Italian. Given a topic or a search query, the model generates a short-form text post that is contextually relevant, complete with typical social media elements like hashtags.

The model was efficiently fine-tuned using the Unsloth library with LoRA (Low-Rank Adaptation) on a subset of the Social Media Post Relevance dataset.

This repository contains the GGUF quantized version of the LoRA adapters, making it suitable for fast inference on CPUs and compatible with tools like llama.cpp.

Intended Use

The primary use case for this model is to automate or assist in the creation of social media content. It can be used by content creators, social media managers, or developers building applications that require topic-based text generation.