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---
library_name: transformers
tags:
- medical
license: mit
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: summarization
---
# 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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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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## How to Get Started with the Model
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## Training Details
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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