---
license: llama3.2
tags:
- unsloth
- text-generation
datasets:
- marmikpandya/mental-health
- Amod/mental_health_counseling_conversations
- AdithyaSK/CompanionLLama_instruction_30k
base_model:
- unsloth/Llama-3.2-3B-Instruct
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

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model has been fine-tuned for use in a chatbot aimed at mental well-being support. It is designed to offer empathetic, supportive responses to users' mental health inquiries. A combined dataset was created by merging three relevant datasets for training to enhance the model’s ability to understand and respond appropriately in counseling scenarios.

- **Model Name:** Llama_finetunedModel
- **Developed by:** Ayesha Noor
- **Model type:** Language model for conversational AI
- **Language(s) (NLP):** English
- **Finetuned model:** https://huggingface.co/ayeshaNoor1/Llama_finetunedModel

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/ayeshaNoor1

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Intended for mental health chatbot applications, particularly for providing initial support, resources, and empathetic responses in mental well-being conversations.

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

May be used as part of broader mental health support applications, integrated into platforms aimed at user well-being.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Not recommended for critical mental health assessments, as it is not a replacement for professional help. Avoid using for high-stakes decision-making without appropriate oversight.


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be aware of the limitations in handling diverse mental health needs and sensitive conversations. Professional oversight is advised when using in serious or emergency mental health contexts.


## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayeshaNoor1/Llama_finetunedModel")
model = AutoModelForCausalLM.from_pretrained("ayeshaNoor1/Llama_finetunedModel")

# Sample input text
input_text = "I'm feeling really down lately. Can you help me?"

# Tokenize and generate response
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

# Decode and print the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
A single dataset was created by merging:

- **First Dataset:** - https://huggingface.co/datasets/marmikpandya/mental-health
- **Second Dataset:** - https://huggingface.co/datasets/Amod/mental_health_counseling_conversations
- **Third Dataset:** - https://huggingface.co/datasets/AdithyaSK/CompanionLLama_instruction_30k

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing

Data was preprocessed to ensure consistency in format, relevance to mental health support, and removal of any sensitive or personal identifiers.

#### Summary
The model demonstrated proficiency in providing supportive responses in well-being conversations.


## Technical Specifications


### Compute Infrastructure

#### Software

- **Libraries:** transformers, datasets, torch, pandas, trl, unsloth
- **Framework:** PyTorch