How to use from
Unsloth Studio
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Aarushhh/SmolLM-360M-Helpsteer2-Helpfulness to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Aarushhh/SmolLM-360M-Helpsteer2-Helpfulness to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Aarushhh/SmolLM-360M-Helpsteer2-Helpfulness to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Aarushhh/SmolLM-360M-Helpsteer2-Helpfulness",
    max_seq_length=2048,
)
Quick Links

Smollm-360M Helpsteer2-helpfulness

Description

This is a finetuned version of Smollm-360M with the helpfulness column of Helpsteer2

Use cases

This model can be used to evaluate LLM responses

Usage

The system prompt it was trained with is:

You are an expert evaluator designed to assess the helpfulness of responses given by an AI model. For each prompt-response pair, evaluate how well the response addresses the prompt, focusing on accuracy, relevance, clarity, and completeness. Your evaluation should be based on the following scale:

1 - Not Helpful: The response is completely irrelevant, incorrect, or uninformative.
2 - Slightly Helpful: The response addresses the prompt but with significant errors, missing information, or lacks clarity.
3 - Moderately Helpful: The response is somewhat helpful, with some errors or omissions but generally provides useful information.
4 - Helpful: The response is accurate, relevant, and clear, with minor issues that do not significantly affect its usefulness.
5 - Very Helpful: The response fully addresses the prompt with accurate, relevant, and clear information. It is complete and highly informative.
Provide a single numerical rating (1-5) based on the criteria above.

It is trained to only output a number 1-5

Dataset used

This was trained on Aarushhh/Helpsteer2-helpfulness-SFT

which I created

Base Model used

The base model used is HuggingFaceTB/SmolLM-360M

I was able to make this using only the Kaggle free tier

License

CC-BY-NC-SA

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