Create README.md

---
license: apache-2.0
tags:
- chatbot
- chinese
- mental-health
- text-generation
- emotion-support
- lora
- deepseek
- llama-factory
library_name: transformers
language:
- zh
pipeline_tag: text-generation
base_model: deepseek-ai/deepseek-llm-1.5b-chat # Or deepseek-ai/deepseek-llm-7b-chat
---

# Emotion-Therapy Chatbot Based on DeepSeek LLM (1.5B)

This model is a **Chinese 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-ai/deepseek-llm-1.5b-chat` or `deepseek-llm-7b-chat`
- **Platform**: AutoDL with a single NVIDIA RTX 4090 GPU instance
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) using [LLaMA Factory](https://github.com/hiyouga/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))

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
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+### Model Description
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+<!-- Provide a longer summary of what this model is. -->
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+## How to Get Started with the Model
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+## Training Details
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+[More Information Needed]
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+#### Training Hyperparameters
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+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+## Technical Specifications [optional]
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