SoufianeDahimi/child_trauma_assessment_gemma-GGUF

Child Trauma Assessment Gemma - Specialized Model for Trauma Assessment

This is a fine-tuned version of Gemma 3N specifically optimized for conducting trauma assessments with children from conflict zones. The model is designed to facilitate empathetic, culturally-sensitive conversations and generate professional psychological reports.

Model Description

• Base Model: Gemma 3N (unsloth/gemma-3n-E2B-it)
• Fine-tuning Method: LoRA (r=16, alpha=16)
• Training Focus: Specialized for trauma assessment conversations and report generation
• Languages: Multilingual support (Arabic dialects, Ukrainian, English)
• Context Length: 2048 tokens

Training Details

• Dataset and Training Script: Details about training script and datast curation can be found here: https://github.com/Dahimi/Gemma3n_Finetune_Child_Trauma

• Fine-tuning Configuration:

  • Batch Size: 1 with gradient accumulation (4 steps)
  • Learning Rate: 2e-4 with linear scheduler
  • Training Focus: Conversation responses only (loss masked on user inputs)
  • LoRA Parameters: r=16, alpha=16
  • Context Length: 2048 tokens

Intended Uses

This model is designed for:

• Conducting initial trauma assessments with children from conflict zones
• Supporting mental health professionals and volunteers
• Generating standardized trauma assessment reports
• Facilitating multilingual mental health support

Primary Functions

1. Conversational Assessment:

   • Empathetic dialogue with parents/caregivers
   • Culturally-appropriate questioning
   • Trauma indicator identification

2. Report Generation:

   • Structured professional assessments
   • Severity scoring (1-10 scale)
   • Risk indicator analysis
   • Cultural context documentation

Language Support

The model supports:

• Palestinian/Levantine Arabic
• Sudanese Arabic
• Ukrainian
• English

Note: All assessment reports are generated in professional English regardless of conversation language

Limitations & Biases

• This model should not replace professional mental health assessment
• Should be used as a support tool under professional supervision
• May have limitations in understanding extremely specific cultural contexts
• Not a substitute for emergency mental health intervention

Training Data

The model was trained on a specialized dataset containing:

1. Conversation Examples: Multi-turn dialogues between parents and AI
2. Report Generation Examples: Professional assessment report templates

Data format follows Gemma's chat template with alternating roles.

Usage

from transformers import TextStreamer
from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    "SoufianeDahimi/child_trauma_assessment_gemma-GGUF",
    max_seq_length = 2048,
    load_in_4bit = True,
)

def generate_response(prompt):
    messages = [{
        "role": "user",
        "content": [{"type": "text", "text": prompt}]
    }]
    inputs = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt = True,
        return_tensors = "pt",
        tokenize = True,
        return_dict = True,
    ).to("cuda")
    
    return model.generate(
        **inputs,
        max_new_tokens = 512,
        temperature = 0.7,
        top_p = 0.95,
        top_k = 64,
        streamer = TextStreamer(tokenizer, skip_prompt = True),
    )

Output Format

The model generates two types of responses:

1. Conversational Responses:

   • Empathetic dialogue
   • Follow-up questions
   • Guidance and support

2. Assessment Reports:

   • Parent observations summary
   • Trauma indicator analysis
   • Severity scoring
   • Risk assessment
   • Cultural context notes

Ethical Considerations

This model is designed for sensitive mental health contexts and should be used with appropriate care:

• Always use under professional supervision
• Maintain strict privacy and data protection
• Consider cultural and contextual sensitivities
• Follow ethical guidelines for mental health assessment

Citations

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