Instructions to use thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

Unsloth Studio

How to use thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT with 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 thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT 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 thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="thlurte/FastData-LM-1.5B-CodeMath-TaskArith-SFT",
    max_seq_length=2048,
)

FastData-LM-1.5B-CodeMath-TaskArith-SFT / adapter_config.json

Commit History

Upload model trained with Unsloth

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thlurte commited on 7 days ago

Commit History

Upload model trained with Unsloth 2a4a32c verified

Upload model trained with Unsloth

2a4a32c
verified