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ai-medical/
fine_tuned_deepseek_v1_empathy

Name: fine_tuned_deepseek_v1_empathy
Availability: InStock

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deepseek-ai/DeepSeek-R1-Distill-Llama-8B

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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

python

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:

bibtex

@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