whisper-psychology-gemma-3-1b/README.md

license, datasets, language, base_model, pipeline_tag, library_name, tags

license	datasets	language	base_model	pipeline_tag	library_name	tags
apache-2.0	jkhedri/psychology-dataset	en	google/gemma-3-1b-it	text-generation	transformers	psychology, mental-health, chatbot, fine-tuned, gemma, lora,

Whisper Psychology Chatbot

Model Description

Whisper is a mental health chatbot fine-tuned on the Gemma-3-1B-IT model using psychology-focused conversational data. The model is designed to provide supportive and empathetic responses for mental health conversations.

Developed by: DeepFinders - SLTC Research University

Training Details

• Base Model: google/gemma-3-1b-it
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Dataset: jkhedri/psychology-dataset
• Training Samples: ~2000 psychology conversations
• LoRA Configuration:
  • r=8, lora_alpha=16
  • Target modules: q_proj, k_proj, v_proj, o_proj
  • Dropout: 0.1

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "KNipun/whisper-psychology-gemma-3-1b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16
)

# Format conversation
def chat_with_whisper(user_message):
    prompt = f"<start_of_turn>user\\n{user_message}<end_of_turn>\\n<start_of_turn>model\\n"
    
    inputs = tokenizer(prompt, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=150,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response[len(prompt):]

# Example usage
response = chat_with_whisper("I'm feeling anxious about my upcoming exam. Can you help me?")
print(response)

Model Identity

The model introduces itself as: "I'm Whisper, your mental health chatbot, developed by DeepFinders — an innovative student team at SLTC Research University."

Limitations

• This model is for educational and research purposes
• Not a replacement for professional mental health care
• May generate incorrect or inappropriate responses
• Should be used with appropriate safeguards and human oversight

Training Infrastructure

• Hardware: Google Colab (GPU)
• Quantization: 4-bit quantization during training
• Memory Optimization: Gradient checkpointing, mixed precision (FP16)

Citation

@misc{whisper-psychology-2024,
  title={Whisper Psychology Chatbot},
  author={DeepFinders Team, SLTC Research University},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/your-username/whisper-psychology-gemma-3-1b}
}

Ethical Considerations

This model should be used responsibly with appropriate disclaimers about its limitations in providing mental health support.