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
Simplify heading and add text-generation task tag
Browse files
README.md
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---
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language:
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- en
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tags:
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- mlx
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- pretraining
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- from-scratch
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# OpenSML-152M coming soon
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**William Zebrowski | SML-MLX V2 | Pretraining in progress**
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An independently trained, English-first small language model built with Apple's
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[MLX](https://github.com/ml-explore/mlx) framework and trained across four Mac Studios.
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---
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- text-generation
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- mlx
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- pretraining
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- from-scratch
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# OpenSML-152M coming soon
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An independently trained, English-first small language model built with Apple's
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[MLX](https://github.com/ml-explore/mlx) framework and trained across four Mac Studios.
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