social-autopilot-ai

Qempireautomation/social-autopilot-ai

social-autopilot-ai is a text generation model from Qempireautomation. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.

AI-Powered Social Media Content Generation Model for Q-Empire Automation Clients

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Updated Jul 8, 2026

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Hugging Face

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At a glance

Task: Text Generation
Library: transformers
License: mit
Access: Public
Created: Jul 8, 2026
Updated: Jul 8, 2026
SHA: 1ed1d79a

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Model card

From the Hugging Face model README

Q-Empire Social Autopilot AI

AI-Powered Social Media Content Generation Model for Q-Empire Automation Clients

Model Description

This model powers the Q-Empire Social Autopilot platform — an autonomous social media marketing system that generates unique, platform-optimized content for 25 social media platforms.

Architecture

Base Model: mistralai/Mistral-7B-Instruct-v0.3
Task: Multi-platform social media content generation
Optimization: Platform-specific prompt engineering with content rules
Inference: Hugging Face Inference API

Capabilities

Platform-aware content generation (25 platforms)
Brand voice consistency from website analysis
Engagement optimization per platform
Hashtag strategy generation
Character limit enforcement
Call-to-action generation

Supported Platforms (25)

Facebook, Instagram, X/Twitter, LinkedIn, TikTok, Pinterest, YouTube, Reddit, Threads, Tumblr, Medium, Mastodon, Discord, Telegram, WhatsApp Business, Snapchat, Bluesky, WordPress, Blogger, Mix, Quora, VK, Weibo, LINE, KakaoTalk

Usage

from huggingface_hub import InferenceClient

client = InferenceClient()

prompt = '''<s>[INST] You are Q-Empire's AI Social Media Content Engine.
Generate an engaging Instagram post for a tech company.

Brand: Q-Empire AI
Theme: AI automation for business
Style: visual storytelling, emoji-rich
Max chars: 2200

Generate the post: [/INST]'''

response = client.text_generation(
    prompt,
    model="mistralai/Mistral-7B-Instruct-v0.3",
    max_new_tokens=500,
    temperature=0.7,
)
print(response)