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--- |
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license: apache-2.0 |
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tags: |
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- chatbot |
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- mental-health |
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- text-generation |
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- emotion-support |
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- lora |
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- deepseek |
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- llama-factory |
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library_name: transformers |
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language: |
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- en |
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pipeline_tag: text-generation |
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base_model: deepseek-ai/deepseek-llm-1.5b-chat |
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--- |
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# Emotion-Therapy Chatbot Based on DeepSeek LLM (1.5B) |
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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. |
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## 💡 Project Background |
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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. |
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## 🔧 Model Training Details |
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- **Base Model**: `Deepseek R1-1.5B - Distill` or `Deepseek R1-7B - Distill` |
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- **Platform**: AutoDL with a single NVIDIA RTX 4090 GPU instance |
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) using [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) |
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- **Objective**: Improve model performance on empathetic responses, emotional understanding, and mental support |
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## 📚 Training Dataset |
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Custom-built Chinese emotional support corpus, including: |
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- Typical therapist-style conversational prompts and responses |
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- Encouraging and empathetic phrases for anxiety, sadness, and loneliness |
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- User-simulated mental health inputs with varied emotional tone |
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The dataset was manually cleaned to ensure linguistic fluency, emotional relevance, and safe content. |
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## 🚀 How to Use |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("chi0818/my-chatbot-model") |
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tokenizer = AutoTokenizer.from_pretrained("chi0818/my-chatbot-model") |
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input_text = "Today I feel so lonely and sad……" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
