license: llama3
language:
- en
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B
pipeline_tag: text-generation
tags:
- peft
- lora
- llama-3
- tabular-data
- adolescent-mental-health
MindScale
A Psychometric Foundation Model for
Population-Scale Adolescent Mental Health Assessment
📋 Overview
Model
MindScale is a language-model-based psychometric foundation model adapted from Meta-Llama-3-8B for questionnaire-based adolescent mental health screening and psychiatric symptom assessment.
Released files
The release contains LoRA adapter weights and requires the base model for use.
📊 Training Details
Dataset: Large-scale adolescent questionnaire dataset
Scale: 402,301 students from 377 schools
Instruments: 22 questionnaires comprising 250 items
Information domains: Environmental factors, psychological characteristics, and questionnaire-derived psychiatric symptoms
Training method: LoRA-based parameter-efficient fine-tuning
Training stages: Pretraining, at-risk identification, and psychiatric symptom assessment
Data access: The full participant-level training cohort is not included because it contains sensitive information from adolescents.
🤗 Loading the Adapters
Adapter directories:
Pretrain/,Screen/, andAssessment/.Stage dependencies: Screening requires the pretraining and screening adapters; assessment requires all three.
Before running: Use the MindScale code directory with its runtime dependencies installed. The example uses the code release's helpers to prepare special tokens and stage adapters consistently.
from pathlib import Path
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer
from configs import (
SerializerConfig, TokenizerConfig, TrainConfig_Screen, LoraConfig_Screen,
)
from models.llama_model import load_llama_3_lora
base_model_id = "meta-llama/Meta-Llama-3-8B"
adapter_id = "Rango001/Adolescent-mental-health"
weights_dir = Path(snapshot_download(
repo_id=adapter_id,
allow_patterns=["*/adapter_config.json", "*/adapter_model.safetensors"],
))
tokenizer = AutoTokenizer.from_pretrained(base_model_id, use_fast=False)
model, tokenizer = load_llama_3_lora(
tokenizer, SerializerConfig(), TokenizerConfig(), base_model_id,
task_type="Screen",
train_config=TrainConfig_Screen(), lora_config=LoraConfig_Screen(),
pretrain_state_dict_path=str(weights_dir / "Pretrain"),
screen_state_dict_path=str(weights_dir / "Screen"),
test_task=True,
)
model.eval()
Base model access: Accept the Meta Llama 3 license on Hugging Face.
Runtime: A CUDA-enabled environment is required. The example uses the code release's existing 8-bit loading.
Questionnaire inference: Follow the accompanying code README for input preparation and prediction export.
📝 Intended Use
MindScale is intended for:
- Population screening: Research on adolescent mental health assessment.
- Method development: Studies of structured questionnaire modelling.
- Model evaluation: Evaluation on questionnaire-derived tabular data.
⚠️ Limitations
Input scope: The model was trained on questionnaire-based data and may not generalize to other input formats or populations.
Class imbalance and bias: Predictions may be affected by class imbalance and cohort-specific biases.
Clinical validation: The model has not been validated for routine clinical use.
Distribution shifts: Performance may decline under demographic, linguistic, institutional, or other distribution shifts.
🛡️ Ethical Considerations
Research use only: MindScale must not be used for clinical diagnosis, treatment decisions, crisis triage, or autonomous decisions affecting students.
Prediction uncertainty: Outputs may be incorrect or biased, particularly for underrepresented groups.
Participant protection: Any use involving participant data requires appropriate ethical approval, privacy protection, fairness assessment, and qualified human oversight.
📄 License
Use of this adapter and the Meta-Llama-3-8B backbone is subject to the Meta Llama 3 Community License and its Acceptable Use Policy.