Instructions to use Qempireautomation/social-autopilot-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qempireautomation/social-autopilot-ai with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Qempireautomation/social-autopilot-ai")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Qempireautomation/social-autopilot-ai", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qempireautomation/social-autopilot-ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qempireautomation/social-autopilot-ai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qempireautomation/social-autopilot-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Qempireautomation/social-autopilot-ai

SGLang

How to use Qempireautomation/social-autopilot-ai with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Qempireautomation/social-autopilot-ai" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qempireautomation/social-autopilot-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Qempireautomation/social-autopilot-ai" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qempireautomation/social-autopilot-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Qempireautomation/social-autopilot-ai with Docker Model Runner:
```
docker model run hf.co/Qempireautomation/social-autopilot-ai
```

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)