README.md

metadata

datasets:
  - Amod/mental_health_counseling_conversations
base_model:
  - meta-llama/Llama-3.1-8B-Instruct
tags:
  - mental_health

Here’s a README.md file tailored for your AI project on Hugging Face. This README assumes your model is designed for facial expression recognition with fine-tuning on mental health counseling conversations. Make sure to replace placeholders like YOUR_USERNAME with actual details as needed.

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Facial Expression and Mental Health Counseling AI Model

Project Overview

This AI model combines facial expression recognition with mental health counseling-focused dialogue generation. Fine-tuned on the Amod/mental_health_counseling_conversations dataset using LoRA (Low-Rank Adaptation) and Unsloth, this model is designed to offer empathetic responses based on visual and conversational cues, suitable for virtual counselors or mental health assistants.

Key capabilities:

• Real-time Emotion Recognition from facial expressions
• Contextually Relevant Responses in a supportive, conversational tone

Model Summary

• Model Type: Conversational AI with facial expression support
• Training Dataset: Amod/mental_health_counseling_conversations
• Fine-Tuning Techniques: LoRA and Unsloth for efficient, optimized adaptation
• Usage Applications: Mental health support, virtual assistants, interactive emotional AI

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Quick Start

1. Load the Model

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("LOHAMEIT/BITShyd")
   model = AutoModelForCausalLM.from_pretrained("LOHAMEIT/BITShyd")
   

2. Prepare the Input

   • Ensure the input text or image follows the required pre-processing steps for facial expression recognition.
   • Use transformers for text and facial expression embeddings to create a blended emotional context.

3. Generate a Response

   inputs = tokenizer("User input text here", return_tensors="pt")
   output = model.generate(**inputs)
   print(tokenizer.decode(output[0], skip_special_tokens=True))
   

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Training and Fine-Tuning

This model was fine-tuned with LoRA and Unsloth:

• LoRA enables efficient training with limited resources by reducing the dimensionality of model parameters, while retaining high accuracy.
• Unsloth minimizes latency and optimizes response generation, improving the model's suitability for real-time applications.

1. Install LoRA & Unsloth:

   pip install lora unsloth
   

2. Fine-Tune on Custom Dataset (if desired):

   from lora import LoraTrainer
   trainer = LoraTrainer(model, dataset="Amod/mental_health_counseling_conversations")
   trainer.train()
   

Model Details

Parameter	Description
Model Size	8 Billion Parameters
Fine-Tuning	LoRA + Unsloth
Dataset	Amod/mental_health_counseling_conversations
Primary Use	Mental Health AI, Virtual Support

Example Use Case

The model is designed to recognize and interpret facial expressions alongside counseling conversations. This interaction facilitates emotionally supportive responses, tailored for user needs in mental health applications or personal emotional assistants.

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License

This model and dataset are licensed for non-commercial use. For more details, see LICENSE.

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Explore the model on Hugging Face: LOHAMEIT/BITShyd

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