Text Generation
MLX
Safetensors
English
information-extraction
structured-output
json
small-language-model
apple-silicon
from-scratch
post-training
research
Instructions to use wzebrowski/OpenSML-JSONLite-175M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use wzebrowski/OpenSML-JSONLite-175M 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-JSONLite-175M") 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-JSONLite-175M 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-JSONLite-175M" --prompt "Once upon a time"
- Atomic Chat
File size: 689 Bytes
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message: "Please cite this source repository and the selected checkpoint and native inference identity when reporting OpenSML-JSONLite-175M."
title: "OpenSML-JSONLite-175M: Native JSON Generation from Source Text and Schema"
authors:
- family-names: Zebrowski
given-names: William
version: 0.4.0
date-released: 2026-10-16
url: "https://github.com/williamzebrowskI/OpenSML-JSONLite-175M"
repository-code: "https://github.com/williamzebrowskI/OpenSML-JSONLite-175M"
license: Apache-2.0
abstract: "Source, tokenizer, training provenance and observed evaluation records for a 175.6M-parameter native text-to-JSON model. Source license only; weights are not included."
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