---
base_model: tiiuae/falcon-rw-1b
library_name: peft
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Falcon-1B-Mental-Health-Advisor is a fine-tuned version of the tiiuae/falcon-rw-1b model, adapted for providing empathetic and contextually relevant responses to mental health-related queries. The model has been trained on a curated dataset to assist in mental health conversations, offering advice, guidance, and support for individuals dealing with issues like stress, anxiety, and depression. It provides a compassionate approach to mental health queries while focusing on promoting emotional well-being and mental health awareness.

## Model Details

# Falcon-1B Fine-Tuned for Mental Health (LoRA)

This is a LoRA adapter for the Falcon-RW-1B model. It was fine-tuned on the 'marmikpandya/mental-health' dataset.

## Usage

Since this model is an adapter, it **must** be loaded with the original Falcon-RW-1B model using PEFT:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "tiiuae/falcon-rw-1b"
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, "ShivomH/Falcon-1B-Finetuned-Mental-Health")

tokenizer = AutoTokenizer.from_pretrained("ShivomH/Falcon-1B-Finetuned-Mental-Health")

### Model Description

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.14.0
```