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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- llama-3.2 |
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- fine-tuning |
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- meditation |
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- guided-meditation |
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- wellness |
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- text-generation |
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base_model: meta-llama/Meta-Llama-3.2-3B-Instruct |
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datasets: |
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- AlbertoB12/GuidedMeditations1 |
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--- |
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# Meditation Guide (Llama 3.2 - 3B) |
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This is a fine-tuned version of `meta-llama/Meta-Llama-3.2-3B-Instruct`, specifically adapted to generate guided meditation scripts. The model was trained on the `AlbertoB12/GuidedMeditations1` dataset, a collection of diverse guided meditation texts. |
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The goal of this project is to provide a specialized AI tool for creating content in the wellness and mindfulness space. It can generate complete meditation scripts based on a simple prompt, focusing on themes like relaxation, anxiety relief, focus, and gratitude. |
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## Model Description |
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- **Base Model**: `meta-llama/Meta-Llama-3.2-3B-Instruct` |
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- **Language**: English (en) |
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- **Task**: Text Generation, Guided Meditation Scripting |
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- **Trained on:** [https://huggingface.co/datasets/AlbertoB12/GuidedMeditations1](https://huggingface.co/datasets/AlbertoB12/GuidedMeditations1) |
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The model excels at adopting a calm, encouraging, and guiding tone suitable for meditation. It understands instructions related to pacing, focus points (e.g., breath, body sensations), and common meditation themes. |
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## Intended Uses & Limitations |
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### Intended Uses |
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This model is designed for: |
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- **Content Creation**: Generating scripts for wellness apps, YouTube channels, or personal mindfulness practice. |
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- **Personalization**: Creating custom meditation scripts tailored to specific needs (e.g., "a 5-minute meditation for morning focus"). |
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- **Creative Assistance**: A tool for mindfulness teachers and practitioners to brainstorm and develop new meditation content. |
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> **Disclaimer:** This model is for informational and creative purposes only. The content it generates is **not** a substitute for professional medical or psychological advice, diagnosis, or treatment. |
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### Limitations |
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- **Narrow Domain**: The model is highly specialized. It may not perform well on topics outside of meditation, mindfulness, and general wellness. |
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- **Potential for Hallucination**: Like all LLMs, it may occasionally generate text that is nonsensical or not perfectly aligned with the prompt. |
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- **Bias**: The model's output will reflect the styles and potential biases present in the `GuidedMeditations1` dataset. |
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## How to Use |
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To use this model, ensure you have accepted the terms of use for Llama 3.2 on the `meta-llama/Meta-Llama-3.2-8B-Instruct` model page. The model should be used with the Llama 3.2 chat template. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import os |
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# --- Configuration --- |
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# Set your Hugging Face token (if the model is private or requires authentication) |
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# For HF Spaces, set this as a secret named HF_TOKEN |
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hf_token = os.getenv("HF_TOKEN") |
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model_id = "AlbertoB12/Llama-3.2-3B-Instruct-MeditationGuide" |
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# --- Load Tokenizer and Model --- |
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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token=hf_token, |
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trust_remote_code=True |
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) |
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model.eval() |
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# --- Prepare the Prompt --- |
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# Use the official chat template for Llama 3.2 |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a helpful meditation guide. Your purpose is to generate calm, soothing, and effective guided meditation scripts based on the user's request." |
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}, |
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{ |
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"role": "user", |
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"content": "Write a 5-minute guided meditation script focused on releasing anxiety." |
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}, |
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] |
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prompt = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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# --- Generate the Response --- |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=1024, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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# --- Decode and Print --- |
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response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True) |
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print(response) |
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``` |
