Instructions to use Nevil9/PsyLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nevil9/PsyLlama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Nevil9/PsyLlama") - Notebooks
- Google Colab
- Kaggle
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Verified signature# PsyLlama: A Conversational AI for Mental Health Assessment **Model Name**: `PsyLlama` **Model Architecture**: LLaMA-based model (fine-tuned) **Model Type**: Instruct-tuned, conversational AI model **Primary Use**: Mental health assessment through psychometric analysis --- ### Model Description **PsyLlama** is a conversational AI model based on LLaMA architecture, fine-tuned for mental health assessments. It is designed to assist healthcare professionals in conducting initial psychometric evaluations and mental health assessments by generating context-aware conversational responses. The model uses structured questions and answers to assess patients' mental states and supports clinical decision-making in telemedicine environments. **Applications**: - Psychometric evaluation - Mental health chatbot - Symptom analysis for mental health assessment --- ### Model Usage To use **PsyLlama**, you can load it from Hugging Face using the `transformers` library. Below is a code snip…d7e678a -
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