social-media-advisor-phi2-GGUF

📱 Social Media Advisor Phi-2 GGUF

A specialized AI advisor for social media strategy, optimized for performance

This repository hosts a quantized version of Microsoft's Phi-2 model fine-tuned specifically for social media strategy advising. The model provides data-driven recommendations on posting frequency, content strategy, hashtag usage, optimal timing, and monetization across major social platforms.

🚀 Model Overview

The Social Media Advisor has been carefully crafted through:

• Fine-tuning on a specialized dataset of social media best practices
• Conversion to GGUF format for wider compatibility
• 4-bit quantization for efficient deployment on consumer hardware

💻 Technical Specifications

📦 Available Models

• phi2-social-media-advisor-q4_k_m.gguf: Our recommended model with the optimal balance between size, speed, and quality
  • 4-bit quantization
  • Medium precision
  • Excellent performance on CPU
  • Suitable for deployment on most consumer hardware

🔍 Model Capabilities

The model excels at providing tailored advice for:

• Posting Strategy: Optimal frequency and timing based on platform and audience size
• Content Optimization: Formats and styles that drive highest engagement
• Hashtag Strategy: Ideal number and combinations for discovery
• Monetization: Pricing guidance for sponsored content based on follower count and engagement
• Growth Tactics: Targeted approaches to increase follower count and engagement rate

🛠️ Usage Guide

This model is compatible with the entire llama.cpp ecosystem, including:

• llama.cpp CLI
• llama-cpp-python
• text-generation-webui
• LM Studio
• Ollama
• ChatBox

Command Line Example

# Basic usage with llama.cpp
./main -m phi2-social-media-advisor-q4_k_m.gguf -p "User: How often should I post on Instagram if I have 10,000 followers and 4.2% engagement rate?\nAssistant:"

# With context parameters
./main -m phi2-social-media-advisor-q4_k_m.gguf -c 2048 --temp 0.7 -p "User: How often should I post on Instagram if I have 10,000 followers and 4.2% engagement rate?\nAssistant:"

Python Integration

from llama_cpp import Llama

# Load the model
model = Llama(
    model_path="phi2-social-media-advisor-q4_k_m.gguf",
    n_ctx=2048,
    n_threads=4  # Adjust based on your CPU
)

# Generate a response
prompt = "User: What's the best time to post on LinkedIn for B2B content?\nAssistant:"
output = model.generate(
    prompt, 
    max_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repeat_penalty=1.1
)

print(output['choices'][0]['text'])

🎯 Sample Outputs

Example 1: Posting Strategy

User: How often should I post on Instagram if I have 10,000 followers and 4.2% engagement rate?
Assistant: For Instagram accounts with 10,000-50,000 followers and 4.2% engagement, posting 4-5 times per week is optimal based on analysis of top-performing accounts. Consistency is key - stick to a regular schedule to maintain algorithm visibility. Your engagement rate is quite good (above average), so focus on quality content that continues to drive this level of interaction rather than increasing frequency.

Example 2: Content Optimization

User: What type of content performs best on TikTok for accounts with 50,000 followers?
Assistant: With 50,000 followers on TikTok, you should prioritize short-form video content that includes behind-the-scenes and tutorial-style formats. Top creators in your category see 40% higher engagement with this content type compared to other formats. Focus on authentic, trending audio paired with educational or entertaining content that provides clear value in under 30 seconds.

Example 3: Monetization Strategy

User: How much should I charge for a sponsored Instagram post with 75,000 followers in the fitness niche?
Assistant: With 75,000 followers in the fitness niche on Instagram, you can typically charge ₹20,000-₹40,000 for a standard feed post. For Stories, pricing ranges from ₹15,000-₹25,000, while Reels command ₹30,000-₹60,000 due to their higher engagement. Factor in your specific engagement rate (industry average is 2-3% for fitness) and adjust accordingly - each 1% above average can justify a 10-15% price premium.

🔄 Model Lineage

• Original Model: microsoft/phi-2
• Fine-tuned Version: Aayush61000/social-media-advisor-phi2
• This Repository: Quantized GGUF version optimized for deployment

💡 Advanced Usage Tips

• Temperature Setting: Use 0.7-0.8 for most queries. Lower (0.3-0.5) for more factual responses, higher (0.8-1.0) for more creative suggestions.
• Context Length: The model works best with 2048 token context window.
• Prompt Engineering: For best results, be specific about:
  • Platform (Instagram, TikTok, etc.)
  • Follower count
  • Engagement rate
  • Content niche or industry

⚙️ Quantization Details

The Q4_K_M quantization offers:

• 4-bit precision for weights
• K-quant methodology for handling outliers
• Balanced size-to-quality ratio
• ~90% smaller than full precision models
• Optimized for CPU inference

🌟 Feedback & Contributions

Your feedback helps improve this model! If you find interesting use cases or have suggestions, please:

• Open an issue on this repository
• Submit model evaluations
• Share sample outputs from your usage

📝 Citation

If you use this model in your research or applications, please cite:

@misc{phi2-social-media-advisor,
  author = {Aayush61000},
  title = {Social Media Advisor Phi-2 GGUF},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Aayush61000/social-media-advisor-phi2-GGUF}}
}

📜 License

This model is released under the same license as the original Phi-2 model (MIT License).