Llama-3.1-1B-Instruct-Mental-Health-Classification

Model Card for Model ID

Llama-3.1-1B-Instruct-Mental-Health-Classification is a fine-tuned version of Meta’s LLaMA 3.1B model, optimized to classify mental health-related text into one of the following categories: Normal, Depression, Anxiety, or Bipolar. It uses instruction-style prompts and is trained for short text classification tasks within mental health contexts.

Model Details

Base Model: meta-llama/Llama-3.1B-Instruct

Fine-tuned by: Dhiraj Patra

Model Size: ~1.1 Billion parameters

Architecture: Decoder-only transformer (LLaMA 3 family)

Fine-tuning Method: Supervised Fine-Tuning (SFT) using transformers and trl

Quantization: 4-bit (using BitsAndBytesConfig)

Precision: torch.float16

LoRA Config:

r=64

alpha=16

dropout=0

bias=none

target_modules: Attention and MLP layers (e.g., "q_proj", "v_proj", etc.)

Model Description

Llama-3.1-1B-Instruct-Mental-Health-Classification is a lightweight instruction-tuned language model built on top of Meta’s LLaMA 3 1B parameter architecture. It is specifically fine-tuned to classify short user-generated texts into four common mental health categories: Normal, Depression, Anxiety, and Bipolar. This model is designed to support early detection and categorization of mental health signals in conversations, journals, or other user inputs.

The fine-tuning was performed using supervised data and a prompt-style instruction format to encourage accurate and interpretable outputs. It is optimized for minimal latency and can run in low-resource environments (such as Kaggle notebooks) due to 4-bit quantization using bitsandbytes.

I have used Kaggle FREE GPU to fine tuned this LLM. https://www.kaggle.com/code/dhirajpatra/fine-tune-llama3-2 This model is intended for educational and research use and not for clinical diagnosis.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [https://dhirajpatra.github.io]
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