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
Download pretraining-training-loss.png from OpenSML/OpenSML-JSONLite-175M: direct link, hf CLI and curl.
- Browser
- Download file 347 kB
-
https://huggingface.co/OpenSML/OpenSML-JSONLite-175M/resolve/main/pretraining-training-loss.png
- Command line
-
hf download hf://OpenSML/OpenSML-JSONLite-175M/pretraining-training-loss.png
-
curl -L -o pretraining-training-loss.png https://huggingface.co/OpenSML/OpenSML-JSONLite-175M/resolve/main/pretraining-training-loss.png
347 kB

- Xet hash:
- 8e0adbf521d51ad69cccd3c1828b778b484e226c561a2c696fda63e4071db303
- Size of remote file:
- 347 kB
- SHA256:
- 641fe860d6fd24547fe4d15bd852190f00ddec31bd74eb7ca9eeae74031c2f0c
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