---
license: apache-2.0
base_model: openai/gpt-oss-20b
tags:
- depression-detection
- mental-health
- text-classification
- qlora
- peft
- psychology
- healthcare
language:
- en
pipeline_tag: text-classification
library_name: transformers
---

# GPT-OSS 20B Depression Detection

## Model Description

This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) for depression detection in text. It uses QLoRA (Quantized LoRA) fine-tuning to efficiently adapt the large language model for binary classification of depression indicators in text.

## Model Details

- **Base Model**: openai/gpt-oss-20b (20 billion parameters)
- **Fine-tuning Method**: QLoRA (Quantized Low-Rank Adaptation)
- **Task**: Binary text classification (depression vs non-depression)
- **Training Data**: 6,006 instruction-formatted samples
- **Validation Data**: 1,000 samples
- **Test Data**: 3,245 samples

## Training Configuration

- **LoRA Rank (r)**: 32
- **LoRA Alpha**: 64
- **LoRA Dropout**: 0.1
- **Target Modules**: q_proj, k_proj, v_proj, o_proj
- **Training Epochs**: 2.0
- **Effective Batch Size**: 8
- **Learning Rate**: 5e-4
- **Optimizer**: Paged AdamW 8-bit
- **Scheduler**: Cosine with warmup
- **Quantization**: 4-bit NF4 with double quantization

## Performance

- **Training Time**: 3 hours 6 minutes (752 steps)
- **Final Training Loss**: 2.3626
- **Best Validation Loss**: 2.3047
- **Token Accuracy**: ~53.6%

## Usage

### Installation

```bash
pip install transformers torch peft accelerate bitsandbytes
```

### Inference

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

# Load model and tokenizer
model_id = "openai/gpt-oss-20b"
adapter_id = "PhaaNe/gpt_depression"

# Configure quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# Load base model
tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, adapter_id)

# Prepare input
text = "I feel hopeless and nothing seems to matter anymore. I cant find joy in anything."
instruction = "Analyze this text for depression indicators. Respond depression or non-depression:"
prompt = f"{instruction}

{text}

"

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=10,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
prediction = response[len(prompt):].strip()
print(f"Prediction: {prediction}")
```

## Dataset

The model was trained on a depression detection dataset with the following characteristics:

- **Total Samples**: 10,251
- **Training**: 6,006 samples (58.6%)
- **Validation**: 1,000 samples (9.8%)
- **Test**: 3,245 samples (31.7%)
- **Class Distribution**:
  - Depression: 62.5%
  - Non-depression: 37.5%

## Intended Use

This model is designed for research and educational purposes in mental health text analysis. It can be used to:

- Identify potential depression indicators in text
- Support mental health research
- Assist in preliminary screening (not for clinical diagnosis)
- Analyze social media or forum posts for mental health insights

## Limitations

- **Not for Clinical Diagnosis**: This model should not be used as a substitute for professional mental health assessment
- **Bias**: May reflect biases present in the training data
- **Context**: Performance may vary across different text domains and populations
- **Language**: Primarily trained on English text

## Ethical Considerations

- Use responsibly and with appropriate human oversight
- Consider privacy implications when analyzing personal text
- Do not use for discriminatory purposes
- Complement, dont replace, professional mental health services

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{gpt_depression_2024,
  title={GPT-OSS 20B Depression Detection},
  author={PhaaNe},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/PhaaNe/gpt_depression}
}
```

## License

This model is released under the Apache 2.0 License.