Instructions to use tezodipta/MindEase-Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tezodipta/MindEase-Assistant with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tezodipta/MindEase-Assistant")
model = AutoModelForCausalLM.from_pretrained("tezodipta/MindEase-Assistant")
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 tezodipta/MindEase-Assistant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tezodipta/MindEase-Assistant

SGLang

How to use tezodipta/MindEase-Assistant 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 "tezodipta/MindEase-Assistant" \
    --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": "tezodipta/MindEase-Assistant",
		"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 "tezodipta/MindEase-Assistant" \
        --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": "tezodipta/MindEase-Assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tezodipta/MindEase-Assistant with Docker Model Runner:
```
docker model run hf.co/tezodipta/MindEase-Assistant
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 MindEase-Assistant: A Mental Health AI Assistant

This is a fine-tuned TinyLlama 1.1B model built to assist users with mental health support by generating thoughtful and empathetic responses.

📌 Model Details

Model Description

Developed by: [Tezodipta]
Finetuned from model: TinyLlama-1.1B
Language: English
License: Apache 2.0
Intended Use: Text-based mental health support & guidance
Model Type: Causal Language Model (LLM)

⚡️ How to Use

This model can be used with the transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "tezodipta/MindEase-Assistant"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "I feel anxious, what should I do?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

output = model.generate(input_ids, max_length=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for tezodipta/MindEase-Assistant

Quantizations

1 model