Model Card for Fine-Tuned LLaMA Empathy

Model Summary

Fine-Tuned LLaMA Empathy 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. It is based on the Meta-Llama-3.1-8B-Instruct model and utilizes LoRA (Low-Rank Adaptation) for efficient fine-tuning.

Model Details

Model Description

  • Developed by: AI Medical in collaboration with Ruslanmv.com
  • Funded by:
  • Shared by: AI Medical
  • Model type: Fine-tuned Meta-Llama-3.1-8B-Instruct
  • Language(s) (NLP): English
  • License: Creative Commons Attribution 4.0 International License (CC BY 4.0)
  • Fine-tuned from model: meta-llama/Meta-Llama-3.1-8B-Instruct

Model Sources

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. LLaMA models, in particular, have shown biases.
  • 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 = "ruslanmv/fine_tuned_llama_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 LoRA.
  • Batch size: 32
  • Learning rate: 2e-5
  • Training steps: 1k
  • Hardware: 1x A100 GPU using DeepSpeed ZeRO-3

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

Technical Specifications

Model Architecture and Objective

  • Base Model: meta-llama/Meta-Llama-3.1-8B-Instruct
  • Fine-tuned using LoRA for parameter-efficient training. Key LoRA parameters: r=8, lora_alpha=16, lora_dropout=0.2, target_modules=["v_proj", "q_proj"]

Compute Infrastructure

  • Hardware: AWS spot instances (1x A100 GPUs)
  • Software: Hugging Face transformers, PEFT, PyTorch

Citation

If you use this model, please cite:

@misc{ai-medical_2025,
  author = {AI Medical, ruslanmv.com},
  title = {Fine-Tuned LLaMA Empathy},
  year = {2025},
  howpublished = {\url{[https://huggingface.co/ruslanmv/fine_tuned_llama_empathy](https://huggingface.co/ruslanmv/fine_tuned_llama_empathy)}}
}

More Information

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