Instructions to use chi0818/my-chatbot-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chi0818/my-chatbot-model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="chi0818/my-chatbot-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chi0818/my-chatbot-model")
model = AutoModelForCausalLM.from_pretrained("chi0818/my-chatbot-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chi0818/my-chatbot-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chi0818/my-chatbot-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chi0818/my-chatbot-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chi0818/my-chatbot-model

SGLang

How to use chi0818/my-chatbot-model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "chi0818/my-chatbot-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chi0818/my-chatbot-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "chi0818/my-chatbot-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chi0818/my-chatbot-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chi0818/my-chatbot-model with Docker Model Runner:
```
docker model run hf.co/chi0818/my-chatbot-model
```

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

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

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))

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Safetensors

Model size

2B params

Tensor type

BF16