metadata
license: mit
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
tags:
- empathy
- mental-health
- motivational-interviewing
- cognitive-behavioral-therapy
- fine-tuned
- emotional-support
- empathLM
language:
- en
🧠 EmpathLM
Fine-tuned for Psychologically Safe & Persuasive Emotional Support
EmpathLM is a fine-tuned version of SmolLM2-135M-Instruct trained to generate responses that combine Motivational Interviewing (MI) and Cognitive Behavioral Therapy (CBT) principles.
What Makes EmpathLM Unique
Unlike general-purpose language models, EmpathLM is specifically optimized to:
- ✅ Validate emotions without judgment
- ✅ Reflect feelings back to the person warmly
- ✅ Gently shift perspective without being manipulative
- ✅ Ask powerful open questions that encourage self-reflection
- ❌ Never give unsolicited advice
Benchmark Results
EmpathLM was benchmarked against GPT-4o-mini and a Groq baseline on 20 unseen test situations, scored across: emotional_validation, advice_avoidance, perspective_shift, and overall_empathy.
See the GitHub repository for full benchmark results.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("maliksaad/empathLM")
model = AutoModelForCausalLM.from_pretrained("maliksaad/empathLM")
SYSTEM_PROMPT = """You are EmpathLM — an emotionally intelligent AI trained in Motivational Interviewing
and Cognitive Behavioral Therapy. When someone shares emotional pain:
- Validate their feelings without judgment
- Reflect their emotions back to them
- Ask one powerful open-ended question
- NEVER give unsolicited advice"""
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": "I failed my exam again. I feel like I'm just not smart enough."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
Training Details
| Parameter | Value |
|---|---|
| Base Model | SmolLM2-135M-Instruct |
| Training Examples | ~180 (90% of 200) |
| Epochs | 3 |
| Batch Size | 8 |
| Learning Rate | 2e-5 |
| Max Sequence Length | 512 |
| Training Platform | Kaggle (Free GPU) |
Dataset
Trained on maliksaad/empathLM-dataset
Citation
@model{saad2025empathLM,
title = {EmpathLM: A Psychologically-Grounded Empathetic Response Model},
author = {Muhammad Saad},
year = {2025},
url = {https://huggingface.co/maliksaad/empathLM}
}