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README.md

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-license: llama3
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+# Large social behavior model: Unlocking Behavioral Insights with Fine-tuned Llama 3.2 (3B)
+
+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)](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).
+
+---
+
+## 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!
+
+### 1. Install Required Libraries
+```bash
+pip install transformers torch
+```
+
+### 2. Load Model and Tokenizer
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "your_username/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.
+
+---
+
+## Evaluation
+
+Large-social-behavior-model was tested on [e.g., a subset of the CBC or a custom dataset—specify your evaluation setup]. Preliminary results show:
+- **Behavior Simulation:** Achieved [X% accuracy or RMSE—insert your metric] in predicting engagement metrics, outperforming vanilla Llama 3.2 by [Y%—insert comparison if available].
+- **Behavior Understanding:** Successfully interpreted behavioral drivers with [e.g., 70% alignment to human annotations—add your result].
+- **Comparison:** Outshines baselines like [e.g., GPT-3.5 or Vicuna-13B—specify] on behavior-related tasks, while retaining strong content understanding.
+
+[Add more details post-evaluation, referencing LCBM’s radar plot (Fig. 2) style comparisons if applicable.]
+
+---
+
+## 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) *(If used for multimodal aspects)*
+
+---
+
+## 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](https://github.com/meta-ai/llama/blob/main/LICENSE). Please review the terms in the `LICENSE` file for usage details.
+
+---
+
+## Contact Info
+
+Questions? Suggestions? Reach out at [hello@astadeus.com
+](mailto: hello@astadeus.com
+). We’d love to hear from you!
+
+---
+
+### Final Notes
+- **Customization:** CommerAI/large-social-behavior-model (e.g., `CommerAI/large-social-behavior-model`, dataset specifics, evaluation results) with your actual details to make it authentic.
+- **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!