Large social behavior model: Unlocking Behavioral Insights with Fine-tuned Llama 3.2
This is Large social behavior model, a fine-tuned version of Llama 3.2 with 3 billion parameters, crafted to dive deep into the world of human behavior through language and multimodal data. By integrating "behavior tokens" inspired by cutting-edge research, this model excels at understanding, predicting, and simulating behavioral responses—bridging the gap between content and its real-world impact. Whether you're a researcher, developer, or enthusiast, Large-social-behavior-model invites you to explore the fascinating interplay of communication and action.
Highlights
Built on Llama 3.2 (3B): Inherits the robust natural language understanding and generation capabilities of Meta’s advanced lightweight model.
Behavior-Driven Fine-Tuning: Trained to process "behavior tokens" (e.g., likes, shares, clicks) alongside content, enabling a unique perspective on how messages shape actions.
Behavior Simulation: Predicts how audiences might respond to content—think engagement levels, sentiment trends, or interaction probabilities.
Behavior Understanding: Analyzes the linguistic and contextual cues that drive behavioral outcomes, offering insights into "why" behind the "what."
Multimodal Potential: Capable of reasoning over text and (if extended) visual/audio data, inspired by the Content Behavior Corpus (CBC) paradigm.
Outperforms Baselines: Demonstrates superior performance on behavior-related tasks compared to vanilla Llama 3.2 (exact metrics to be added post-evaluation).
Versatile Applications: From social media analytics to behavioral science, this model opens doors to innovative research and real-world solutions.
Model Details
Base Model:Llama 3.2 (3B) – a compact yet powerful language model by Meta AI, known for its efficiency and multilingual prowess.
Purpose of Fine-Tuning: Large-social-behavior-model was fine-tuned to explore the "effectiveness" level of communication (per Shannon & Weaver’s theory), focusing on how content influences receiver behavior. It aims to predict, simulate, and interpret actions like engagement, sentiment, and decision-making.
Fine-Tuning Data: The model leverages a custom dataset inspired by the Content Behavior Corpus (CBC), featuring [e.g., social media posts, YouTube video metadata, or email campaigns] paired with behavioral metrics (e.g., likes, views, click-through rates). [Insert specifics about your dataset here, e.g., size, source, modalities.]
Fine-Tuning Method: Utilized [e.g., PyTorch/Hugging Face Transformers] with Behavior Instruction Fine-Tuning (BFT), adapting the LCBM approach. Key adjustments include [e.g., learning rate of 2e-5, batch size of 16, 100 epochs]. The process emphasizes joint modeling of content and behavior in a text-to-text framework.
Model Type: Primarily a language model, with potential multimodal extensions (e.g., vision via EVA-CLIP integration, if applicable).
Languages: Performs optimally in English, with multilingual capabilities inherited from Llama 3.2 (fine-tuning data may bias toward specific languages—specify if known).
Potential Applications
Behavioral Science Research: Uncover patterns in how people react to online content, from emotional triggers to decision drivers.
Social Media Trend Analysis: Forecast engagement and virality for posts, videos, or campaigns based on content features.
Market Research & Advertising: Optimize messaging to maximize clicks, shares, or purchases by predicting audience behavior.
Recommendation Systems: Enhance personalization by modeling user preferences and interaction tendencies.
Social Phenomenon Simulation: Build predictive models for societal trends, leveraging behavior-content relationships.
Usage Guide
Get started with Large-social-behavior-model in just a few steps!
Here’s how to predict the engagement level of a social media post:
python
1prompt ="Text: 'Just launched our new eco-friendly product line!' Predict the like level (low/medium/high):"2input_ids = tokenizer.encode(prompt, return_tensors="pt")3output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)4predicted_behavior = tokenizer.decode(output[0], skip_special_tokens=True)5print(predicted_behavior)
Note: For optimal performance, use a GPU (e.g., NVIDIA with 8GB+ VRAM). Adjust max_length or beam settings based on your task.
Evaluation
Large-social-behavior-model was tested on [___]. Preliminary results show:
Behavior Simulation: .
Behavior Understanding: Successfully interpreted behavioral drivers with [___].
Comparison: Outshines baselines like [___] on behavior-related tasks, while retaining strong content understanding.
References
Khandelwal, Ashmit, et al. "Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior." ICLR 2024. arXiv:2309.00359
Meta AI. "Llama 3.2 Model Card." [Official Link TBD]
Liu, Haotian, et al. "Visual Instruction Tuning." arXiv preprint arXiv:2304.08485 (2023). Link
Contribution Call
We’re excited to see where the community takes Large-social-behavior-model! Whether it’s enhancing behavior prediction, expanding to new datasets, or exploring novel applications, your contributions are welcome. Fork the repo, experiment, and share your ideas via issues or pull requests.
License
This model is released under the Llama 3 Community License. Please review the terms in the LICENSE file for usage details.
Engagement: The title and highlights are designed to grab attention, while the usage guide ensures accessibility. The references and contribution call add credibility and community appeal.
Scalability: If you later extend the model (e.g., to 8B parameters or more modalities), this structure scales easily with minor updates.
Let me know if you’d like to tweak any section further or add specifics from your fine-tuning process!
