This model is a emotional-support chatbot fine-tuned on top of DeepSeek LLM-1.5B / 7B Distill using LoRA. It is designed to simulate empathetic, comforting conversations for emotional wellness, daily companionship, and supportive dialogue scenarios.

💡 Project Background

This model is part of the project "Designing an Emotion-Therapy Chatbot Based on the DeepSeek LLM-1.5B". The goal is to build a lightweight, emotionally intelligent chatbot capable of offering comforting and supportive interactions in Chinese, grounded in general large language model capabilities.

🔧 Model Training Details

Base Model: Deepseek R1-1.5B - Distill or Deepseek R1-7B - Distill
Platform: AutoDL with a single NVIDIA RTX 4090 GPU instance
Fine-tuning Method: LoRA (Low-Rank Adaptation) using LLaMA Factory
Objective: Improve model performance on empathetic responses, emotional understanding, and mental support

📚 Training Dataset

Custom-built Chinese emotional support corpus, including:

Typical therapist-style conversational prompts and responses
Encouraging and empathetic phrases for anxiety, sadness, and loneliness
User-simulated mental health inputs with varied emotional tone

The dataset was manually cleaned to ensure linguistic fluency, emotional relevance, and safe content.

🚀 How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("chi0818/my-chatbot-model")
tokenizer = AutoTokenizer.from_pretrained("chi0818/my-chatbot-model")

input_text = "Today I feel so lonely and sad……"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))