mrs83/Kurtis-SmolLM2-1.7B-Instruct

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]

• Repository: 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

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[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