vincenzopalazzo/qwen2.5-7b-rino-huberman-finetuned-model

# qwen2.5-7b-rino-huberman-finetuned-model

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Welcome to the **qwen2.5-7b-rino-huberman-finetuned-model**! 🚀 This is a specialized fine-tuned version of Alibaba's Qwen2.5 7B model, optimized for discussions on health, fitness, neuroscience, and performance optimization, inspired by Andrew Huberman and Stan "Rhino" Efferding. Whether you're building chatbots for wellness advice, generating educational content, or exploring AI in human optimization, this model delivers insightful, science-backed responses.

## 🌟 Why This Model?
- **Powerful & Balanced**: Based on the robust 7B parameter Qwen2.5, offering a great balance between performance and efficiency.
- **Domain-Specific Expertise**: Fine-tuned on datasets from Huberman Lab podcasts and related content featuring Stan Efferding, focusing on topics like strength training, nutrition, sleep, and vitality.
- **Engaging Outputs**: Produces clear, motivational, and applicable advice that's easy to read and implement.
- **Open Source Friendly**: Ready for quick integration into your projects.

## 🔍 Model Overview
- **Base Model**: [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
- **Fine-Tuning Method**: Fine-tuned on [relevant datasets, e.g., transcripts from Huberman Lab podcasts featuring Stan Efferding], making it ideal for [health optimization, nutrition advice, or motivational content]..
- **Parameters**: 7B
- **Languages**: Primarily English & Italian.
- **Intended Use**: Generating content on fitness, neuroscience, health protocols, and performance enhancement; ideal for educational apps, virtual coaches, or research tools.

## 🛠️ Usage
Get started with the Hugging Face Transformers library. Here's a simple example:

```python
from transformers import pipeline

# Load the model
generator = pipeline('text-generation', model='vincenzopalazzo/qwen2.5-7b-rino-huberman-finetuned-model')

# Generate a response
prompt = "What are the best ways to build strength and improve vitality according to Huberman and Efferding?"
result = generator(prompt, max_length=200, num_return_sequences=1)
print(result[0]['generated_text'])

Installation

1. Install dependencies: pip install transformers torch
2. Download the model from Hugging Face.
3. Run inference as shown.

For faster deployment, consider Ollama or vLLM.

📊 Performance & Evaluation

TODO

⚠️ Limitations & Ethical Considerations

TODO

Responsible use is encouraged—share your feedback!

📚 Citation

If using this model:

@misc{qwen2.5-7b-rino-huberman-finetuned-model,
  author = {Vincenzo Palazzo},
  title = {qwen2.5-7b-rino-huberman-finetuned-model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/vincenzopalazzo/qwen2.5-7b-rino-huberman-finetuned-model}
}

📝 License

Released under the MIT License. See LICENSE for details.

👏 Acknowledgments

• Built upon Alibaba's Qwen2.5.
• Inspired by Andrew Huberman's Huberman Lab and Stan "Rhino" Efferding's expertise.
• Thanks to the Hugging Face community!

Questions? Open an issue or contribute! Let's optimize human potential with AI. 💪 ```