Update README.md

README.md

@@ -1,58 +1,108 @@
 ---
-base_model: meta-llama/Llama-3.2-3B-Instruct
-library_name: transformers
-model_name: Llama-3.2-3B-Instruct-MeditationGuide
+language: en
+license: apache-2.0
 tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
+- llama-3.2
+- fine-tuning
+- meditation
+- guided-meditation
+- wellness
+- text-generation
+base_model: meta-llama/Meta-Llama-3.2-3B-Instruct
+datasets:
+- AlbertoB12/GuidedMeditations1
 ---
 
-# Model Card for Llama-3.2-3B-Instruct-MeditationGuide
+# Meditation Guide (Llama 3.2 - 3B)
 
-This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+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.
 
-## Quick start
+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.
 
-```python
-from transformers import pipeline
+## Model Description
+
+- **Base Model**: `meta-llama/Meta-Llama-3.2-3B-Instruct`
+- **Language**: English (en)
+- **Task**: Text Generation, Guided Meditation Scripting
+- **Trained on:** [https://huggingface.co/datasets/AlbertoB12/GuidedMeditations1](https://huggingface.co/datasets/AlbertoB12/GuidedMeditations1)
+
+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.
+
+## Intended Uses & Limitations
+
+### Intended Uses
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="AlbertoB12/Llama-3.2-3B-Instruct-MeditationGuide", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+This model is designed for:
+-   **Content Creation**: Generating scripts for wellness apps, YouTube channels, or personal mindfulness practice.
+-   **Personalization**: Creating custom meditation scripts tailored to specific needs (e.g., "a 5-minute meditation for morning focus").
+-   **Creative Assistance**: A tool for mindfulness teachers and practitioners to brainstorm and develop new meditation content.
 
-## Training procedure
+> **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.
 
- 
+### Limitations
 
+-   **Narrow Domain**: The model is highly specialized. It may not perform well on topics outside of meditation, mindfulness, and general wellness.
+-   **Potential for Hallucination**: Like all LLMs, it may occasionally generate text that is nonsensical or not perfectly aligned with the prompt.
+-   **Bias**: The model's output will reflect the styles and potential biases present in the `GuidedMeditations1` dataset.
+
+## How to Use
+
+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.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import os
 
-This model was trained with SFT.
+# --- Configuration ---
+# Set your Hugging Face token (if the model is private or requires authentication)
+# For HF Spaces, set this as a secret named HF_TOKEN
+hf_token = os.getenv("HF_TOKEN") 
+model_id = "AlbertoB12/Llama-3.2-3B-Instruct-MeditationGuide"
 
-### Framework versions
+# --- Load Tokenizer and Model ---
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    token=hf_token,
+    trust_remote_code=True
+)
+model.eval()
 
-- TRL: 0.21.0
-- Transformers: 4.55.4
-- Pytorch: 2.8.0+cu126
-- Datasets: 4.0.0
-- Tokenizers: 0.21.4
+# --- Prepare the Prompt ---
+# Use the official chat template for Llama 3.2
+messages = [
+    {
+        "role": "system",
+        "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."
+    },
+    {
+        "role": "user",
+        "content": "Write a 5-minute guided meditation script focused on releasing anxiety."
+    },
+]
 
-## Citations
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
 
+# --- Generate the Response ---
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    eos_token_id=tokenizer.eos_token_id
+)
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
+# --- Decode and Print ---
+response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+print(response)
 ```