Text Generation
MLX
Safetensors
English
information-extraction
structured-output
json
small-language-model
apple-silicon
from-scratch
post-training
research
Instructions to use OpenSML/OpenSML-JSONLite-175M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenSML/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("OpenSML/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 OpenSML/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 "OpenSML/OpenSML-JSONLite-175M" --prompt "Once upon a time"
- Atomic Chat
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# OpenSML-JSONLite-175M-Instruct
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**Author:** William Zebrowski 路 **
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[Run locally](RUN_GUIDE.md) 路 [Technical report](TECHNICAL_REPORT.md) 路 [Results](JSONLITE_RESULTS.json) 路 [Provenance](checkpoint_provenance.json) 路 [Inference verification](INFERENCE_VERIFICATION.json)
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# OpenSML-JSONLite-175M-Instruct
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**Author:** William Zebrowski 路 **175M parameters**
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[Run locally](RUN_GUIDE.md) 路 [Technical report](TECHNICAL_REPORT.md) 路 [Results](JSONLITE_RESULTS.json) 路 [Provenance](checkpoint_provenance.json) 路 [Inference verification](INFERENCE_VERIFICATION.json)
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