Text Generation
MLX
Safetensors
English
pretraining
from-scratch
small-language-model
post-training
silicon
Instructions to use wzebrowski/OpenSML-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use wzebrowski/OpenSML-150M with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("wzebrowski/OpenSML-150M") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use wzebrowski/OpenSML-150M with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "wzebrowski/OpenSML-150M" --prompt "Once upon a time"
- Atomic Chat
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If you find our work useful, please cite:
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@misc{zebrowski2025opensml,
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title={OpenSML: A Family of Small Language Models},
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author={William Zebrowski},
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year={2025},
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howpublished={\url{https://github.com/wzebrowski/opensml}}
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}
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If you find our work useful, please cite:
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```BibTex
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@misc{zebrowski2025opensml,
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title={OpenSML: A Family of Small Language Models},
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author={William Zebrowski},
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year={2025},
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howpublished={\url{https://github.com/wzebrowski/opensml}}
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}
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```
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