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This repository contains the code and scripts for fine-tuning Facebook's Llama 2 model on counselor-patient interaction data to enhance mental health support applications. The aim is to develop a system capable of understanding and responding to complex mental health-related conversations.

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🧠 Fine-Tuned Llama 2 7B for Mental Health Support

This repository contains the fine-tuned version of Facebook's Llama 2 7B model, trained on counselor-patient data to provide mental health support. πŸ€–πŸ’¬

πŸ“œ Model Description

This model is designed to assist in mental health conversations by generating meaningful and supportive responses based on counselor-patient data. It is a fine-tuned version of the NousResearch/Llama-2-7b-chat-hf model. πŸ’‘

Screenshot 2025-01-16 133528

πŸš€ Intended Uses & Limitations

  • Intended Uses: 🌱 Mental health support, emotional wellbeing assistance, counselor-patient interaction simulation
  • Limitations: 🚧 Not a replacement for professional therapy or counseling. Model outputs should be used as supportive tools, not as substitutes for clinical advice.

πŸ› οΈ Training and Evaluation Data

  • The model is trained on counselor-patient conversations, focusing on mental health scenarios. πŸ—£οΈ

πŸ”§ Training Procedure & Hyperparameters

  • Learning Rate: 0.0002
  • Train Batch Size: 2
  • Eval Batch Size: 8
  • Seed: 42
  • Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • LR Scheduler Type: Cosine
  • LR Scheduler Warmup Ratio: 0.03
  • Epochs: 1 🌟

πŸ“ˆ Training Results

TrainOutput(global_step=1500, training_loss=1.0985136858622233, metrics={'train_runtime': 6391.7993, 'train_samples_per_second': 0.469, 'train_steps_per_second': 0.235, 'total_flos': 3.428099244773376e+16, 'train_loss': 1.0985136858622233, 'epoch': 1.0})

πŸ”— Hugging Face Model Link:

Access the model here for deployment or experimentation! πŸš€

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This repository contains the code and scripts for fine-tuning Facebook's Llama 2 model on counselor-patient interaction data to enhance mental health support applications. The aim is to develop a system capable of understanding and responding to complex mental health-related conversations.

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