🧠 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}
}