---
library_name: transformers
tags:
- MentalHealth
- Counseling
- Chatbot
- InstructionTuning
- QLoRA
datasets:
- Amod/mental_health_counseling_conversations
language:
- en
base_model:
- TinyLlama/TinyLlama-1.1B-Chat
pipeline_tag: text-generation
---

# 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](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat)
- **Fine-tuned by:** Ali Haider
- **Dataset:** [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/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

```python
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))