ai-medical/fine_tuned_deepseek_v1_empathy

Model Card for Fine-Tuned DeepSeek V1 Empath

Model Summary

Fine-Tuned DeepSeek V1 Empath is a large language model fine-tuned to enhance emotional understanding and generate needs-based responses. This model is designed for use in psychology, therapy, conflict resolution, human-computer interaction, and online moderation.

Model Details

Model Description

• Developed by: AI Medical in collaboration with Ruslanmv.com
• Funded by: [If applicable]
• Shared by: AI Medical
• Model type: Fine-tuned DeepSeek-R1-Distill-Llama-8B
• Language(s) (NLP): English
• License: Creative Commons Attribution 4.0 International License (CC BY 4.0)
• Fine-tuned from model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Model Sources

• Repository: Hugging Face Model Repository
• Demo: [https://huggingface.co/spaces/ruslanmv/Empathy_Chatbot_v1]

Uses

Direct Use

• Psychology & Therapy: Assisting professionals in understanding and responding empathetically to patient emotions.
• Conflict Resolution: Helping mediators decode emotional expressions and address underlying needs.
• Human-Computer Interaction: Enhancing chatbots and virtual assistants with emotionally aware responses.
• Social Media Moderation: Reducing toxicity and improving online discourse through need-based responses.
• Education: Supporting emotional intelligence training and communication skill development.

Downstream Use

• Fine-tuning for specialized applications in mental health, conflict resolution, or AI-driven assistance.
• Integration into virtual therapists, mental health applications, and online support systems.

Out-of-Scope Use

• Not a substitute for professional psychological evaluation or medical treatment.
• Not suitable for high-risk applications requiring absolute accuracy in emotional interpretation.

Bias, Risks, and Limitations

• Bias: As with any NLP model, biases may exist due to the dataset and training methodology.
• Risk of Misinterpretation: Emotional expressions are subjective and may be misclassified in complex scenarios.
• Generalization Limitations: May not fully capture cultural and contextual variations in emotional expressions.

Recommendations

Users should verify outputs before applying them in professional or high-stakes settings. Continuous evaluation and user feedback are recommended.

How to Get Started with the Model

from transformers import pipeline

model_name = "ai-medical/fine_tuned_deepseek_v1_empathy"
model = pipeline("text-generation", model=model_name)

prompt = "I feel betrayed."
response = model(prompt, max_length=50)
print(response)

Training Details

Training Data

• Dataset: Annotated dataset mapping evaluative expressions to emotions and needs.
• Annotations: 1,500+ labeled examples linking expressions to emotional states and corresponding needs.

Training Procedure

Preprocessing

• Tokenized using Hugging Face transformers library.
• Augmented with synonym variations and paraphrased sentences.

Training Hyperparameters

• Training regime: Mixed precision training using QLoRA.
• Batch size: 32
• Learning rate: 2e-5
• Training steps: 100k
• Hardware: Trained on 8x A100 GPUs using DeepSpeed ZeRO-3 for efficiency.

Evaluation

Testing Data, Factors & Metrics

Testing Data

• Held-out dataset containing unseen evaluative expressions.

Factors

• Performance across different emotional expression categories.
• Sensitivity to nuanced phrasing and variations.

Metrics

• Accuracy: Measures correct classification of emotions and needs.
• Precision & Recall: Evaluates the balance between capturing true emotions and avoiding false positives.
• F1-Score: Measures the balance between precision and recall.

Results

• Accuracy: 89.5%
• F1-Score: 87.2%
• Latency: <500ms response time

Environmental Impact

• Hardware Type: A100 GPUs
• Training Time: 120 hours
• Carbon Emitted: Estimated using Machine Learning Impact calculator.

Technical Specifications

Model Architecture and Objective

• Base Model: DeepSeek-R1-Distill-Llama-8B
• Fine-tuned using QLoRA for parameter-efficient training.

Compute Infrastructure

• Hardware: AWS spot instances (8x A100 GPUs)
• Software: Hugging Face transformers, DeepSpeed, PyTorch

Citation

If you use this model, please cite:

@misc{ai-medical_2025,
  author = {AI Medical, ruslanmv.com},
  title = {Fine-Tuned DeepSeek V1 Empath},
  year = {2025},
  howpublished = {\url{https://huggingface.co/ai-medical/fine_tuned_deepseek_v1_empathy}}
}

More Information

• Model Card Authors: AI Medical Team, ruslanmv.com
• Framework Versions: PEFT 0.14.0