| # 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. | |
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| ## 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. | |
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| ## Model Details | |
| - **Base Model:** [Llama 3.2 (3B)](https://huggingface.co/meta-llama/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). | |
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| ## 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. | |
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| ## Usage Guide | |
| Get started with Large-social-behavior-model in just a few steps! | |
| ### 1. Install Required Libraries | |
| ```bash | |
| pip install transformers torch | |
| ``` | |
| ### 2. Load Model and Tokenizer | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "CommerAI/large-social-behavior-model" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| ``` | |
| ### 3. Basic Usage Example | |
| Here’s how to predict the engagement level of a social media post: | |
| ```python | |
| prompt = "Text: 'Just launched our new eco-friendly product line!' Predict the like level (low/medium/high):" | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
| output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) | |
| predicted_behavior = tokenizer.decode(output[0], skip_special_tokens=True) | |
| print(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. | |
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| ## 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. | |
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| ## References | |
| - Khandelwal, Ashmit, et al. "Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior." *ICLR 2024*. [arXiv:2309.00359](https://arxiv.org/abs/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](https://arxiv.org/abs/2304.08485) | |
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| ## 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. | |
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| ## License | |
| This model is released under the [Llama 3 Community License](https://github.com/meta-ai/llama/blob/main/LICENSE). Please review the terms in the `LICENSE` file for usage details. | |
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| ## Contact Info | |
| Questions? Suggestions? Reach out at [hello@astadeus.com | |
| ](mailto: hello@astadeus.com | |
| ). We’d love to hear from you! | |
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| ### Final Notes | |
| - **Customization:** CommerAI/large-social-behavior-model. | |
| - **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! | |