README.md

---
library_name: transformers
tags:
- SmolLM2
- text-generation-inference
license: mit
datasets:
- mrs83/kurtis_mental_health_final
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-1.7B-Instruct
pipeline_tag: question-answering
---

# Model Card for Model ID

Kurtis is a fine-tuning, inference and evaluation tool built for SLMs (Small Language Models) such as Huggingface's SmolLM2.

## Model Details

### Model Description

- **Developed by:** Massimo R. Scamarcia <massimo.scamarcia@gmail.com>
- **Funded by [optional]:** Massimo R. Scamarcia <massimo.scamarcia@gmail.com> - (self-funded)
- **Shared by [optional]:** Massimo R. Scamarcia <massimo.scamarcia@gmail.com>
- **Model type:** Transformer decoder
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** HuggingFaceTB/SmolLM2-1.7B-Instruct

### Model Sources [optional]

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

- **Repository:** [https://github.com/mrs83/kurtis](https://github.com/mrs83/kurtis)
- **Paper [optional]:** None
- **Demo [optional]:** [More Information Needed]

## Uses

The model is intended for use in a conversational setting, particularly in mental health and therapeutic support scenarios. 

Suitable use cases include:

- Evaluating the usage of small-language models (SLMs).
- Evaluating small-language models (SLMs) capability to generate empathetic responses in a mental-health context.


### Direct Use

Not suitable for production usage.


### Out-of-Scope Use

This model should not be used for:

- Making critical mental health decisions or diagnoses.
- Replacing professional mental health services.
- Applications where responses require regulatory compliance or are highly sensitive.
- Generating responses without human supervision, especially in contexts that involve vulnerable individuals.


## Bias, Risks, and Limitations

Misuse of this dataset could lead to providing inappropriate or harmful responses, so it should not be deployed without proper safeguards in place.

### Recommendations

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.

[More Information Needed]

## Training Details

### Training Data

WIP

[More Information Needed]

### 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 [optional]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]


## Model Card Contact

Massimo R. Scamarcia <massimo.scamarcia@gmail.com>