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--- |
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base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B |
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library_name: peft |
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--- |
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# Model Card for Fine-Tuned DeepSeek V1 Empath |
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## Model Summary |
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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. |
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## Model Details |
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### Model Description |
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- **Developed by:** AI Medical in collaboration with Ruslanmv.com |
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- **Funded by:** [If applicable] |
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- **Shared by:** AI Medical |
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- **Model type:** Fine-tuned DeepSeek-R1-Distill-Llama-8B |
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- **Language(s) (NLP):** English |
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- **License:** Creative Commons Attribution 4.0 International License (CC BY 4.0) |
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- **Fine-tuned from model:** deepseek-ai/DeepSeek-R1-Distill-Llama-8B |
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### Model Sources |
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- **Repository:** [Hugging Face Model Repository](https://huggingface.co./ai-medical/fine_tuned_deepseek_v1_empathy) |
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- **Demo:** [https://huggingface.co./spaces/ruslanmv/Empathy_Chatbot_v1] |
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## Uses |
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### Direct Use |
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- **Psychology & Therapy:** Assisting professionals in understanding and responding empathetically to patient emotions. |
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- **Conflict Resolution:** Helping mediators decode emotional expressions and address underlying needs. |
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- **Human-Computer Interaction:** Enhancing chatbots and virtual assistants with emotionally aware responses. |
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- **Social Media Moderation:** Reducing toxicity and improving online discourse through need-based responses. |
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- **Education:** Supporting emotional intelligence training and communication skill development. |
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### Downstream Use |
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- Fine-tuning for specialized applications in mental health, conflict resolution, or AI-driven assistance. |
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- Integration into virtual therapists, mental health applications, and online support systems. |
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### Out-of-Scope Use |
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- Not a substitute for professional psychological evaluation or medical treatment. |
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- Not suitable for high-risk applications requiring absolute accuracy in emotional interpretation. |
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## Bias, Risks, and Limitations |
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- **Bias:** As with any NLP model, biases may exist due to the dataset and training methodology. |
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- **Risk of Misinterpretation:** Emotional expressions are subjective and may be misclassified in complex scenarios. |
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- **Generalization Limitations:** May not fully capture cultural and contextual variations in emotional expressions. |
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### Recommendations |
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Users should verify outputs before applying them in professional or high-stakes settings. Continuous evaluation and user feedback are recommended. |
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## How to Get Started with the Model |
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```python |
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from transformers import pipeline |
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model_name = "ai-medical/fine_tuned_deepseek_v1_empathy" |
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model = pipeline("text-generation", model=model_name) |
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prompt = "I feel betrayed." |
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response = model(prompt, max_length=50) |
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print(response) |
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``` |
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## Training Details |
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### Training Data |
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- **Dataset:** Annotated dataset mapping evaluative expressions to emotions and needs. |
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- **Annotations:** 1,500+ labeled examples linking expressions to emotional states and corresponding needs. |
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### Training Procedure |
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#### Preprocessing |
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- Tokenized using Hugging Face `transformers` library. |
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- Augmented with synonym variations and paraphrased sentences. |
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#### Training Hyperparameters |
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- **Training regime:** Mixed precision training using QLoRA. |
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- **Batch size:** 32 |
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- **Learning rate:** 2e-5 |
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- **Training steps:** 100k |
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- **Hardware:** Trained on 8x A100 GPUs using DeepSpeed ZeRO-3 for efficiency. |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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- Held-out dataset containing unseen evaluative expressions. |
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#### Factors |
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- Performance across different emotional expression categories. |
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- Sensitivity to nuanced phrasing and variations. |
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#### Metrics |
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- **Accuracy:** Measures correct classification of emotions and needs. |
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- **Precision & Recall:** Evaluates the balance between capturing true emotions and avoiding false positives. |
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- **F1-Score:** Measures the balance between precision and recall. |
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### Results |
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- **Accuracy:** 89.5% |
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- **F1-Score:** 87.2% |
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- **Latency:** <500ms response time |
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## Environmental Impact |
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- **Hardware Type:** A100 GPUs |
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- **Training Time:** 120 hours |
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- **Carbon Emitted:** Estimated using [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
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## Technical Specifications |
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### Model Architecture and Objective |
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- Base Model: DeepSeek-R1-Distill-Llama-8B |
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- Fine-tuned using QLoRA for parameter-efficient training. |
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### Compute Infrastructure |
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- **Hardware:** AWS spot instances (8x A100 GPUs) |
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- **Software:** Hugging Face `transformers`, DeepSpeed, PyTorch |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{ai-medical_2025, |
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author = {AI Medical, ruslanmv.com}, |
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title = {Fine-Tuned DeepSeek V1 Empath}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co./ai-medical/fine_tuned_deepseek_v1_empathy}} |
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} |
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``` |
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## More Information |
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- **Model Card Authors:** AI Medical Team, ruslanmv.com |
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- **Framework Versions:** PEFT 0.14.0 |
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