Instructions to use ali1001/mental-health-tinyllama-bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ali1001/mental-health-tinyllama-bot with Transformers:

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

pipe = pipeline("text-generation", model="ali1001/mental-health-tinyllama-bot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ali1001/mental-health-tinyllama-bot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ali1001/mental-health-tinyllama-bot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ali1001/mental-health-tinyllama-bot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ali1001/mental-health-tinyllama-bot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ali1001/mental-health-tinyllama-bot

SGLang

How to use ali1001/mental-health-tinyllama-bot 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 "ali1001/mental-health-tinyllama-bot" \
    --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": "ali1001/mental-health-tinyllama-bot",
		"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 "ali1001/mental-health-tinyllama-bot" \
        --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": "ali1001/mental-health-tinyllama-bot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ali1001/mental-health-tinyllama-bot with Docker Model Runner:
```
docker model run hf.co/ali1001/mental-health-tinyllama-bot
```

Model Card: Mental Health Counselor Chatbot (TinyLlama-1.1B)

This model is a lightweight mental health chatbot built on TinyLlama-1.1B-Chat, fine-tuned using QLoRA on the Amod/mental_health_counseling_conversations dataset.

⚠️ Note: This model was fine-tuned on Google Colab (free T4 GPU) for only 1 epoch, intended as a test to evaluate the ability of TinyLlama to respond to counseling prompts.
🧠 Performance can significantly improve with longer training, more data, and better hyperparameter tuning.

Model Details

Model Description

Model Type: Causal Language Model (Instruction-Tuned)
Base Model: TinyLlama/TinyLlama-1.1B-Chat
Fine-tuned by: Ali Haider
Dataset: Amod/mental_health_counseling_conversations
Language: English
License: Apache 2.0

⚠️ This is a prototype model. It was fine-tuned using only 1 epoch on a small sample dataset for demonstration and testing purposes.

Uses

Direct Use

For generating supportive and empathetic responses to mental health-related user inputs. Useful for:

Mental health Q&A bots
Conversational agents in wellness apps

Out-of-Scope Use

Not a substitute for licensed therapy.
Should not be used for clinical decisions or crisis support.

Bias, Risks & Limitations

The model may produce biased or generic responses.
Only trained on one small dataset, so coverage is limited.
May hallucinate or offer vague advice if prompted outside of its domain.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ali1001/mental-health-tinyllama-bot")
tokenizer = AutoTokenizer.from_pretrained("ali1001/mental-health-tinyllama-bot")

prompt = "I'm feeling very anxious lately and can't sleep. What should I do?"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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