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
File size: 869 Bytes
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"model": "OpenSML-JSONLite-175M",
"checkpoint": "main",
"selected_stage": "joint foundation JSON SFT",
"selected_update": 500,
"stages": [
{
"stage": "original-pretraining",
"step": 132005,
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"stage": "structured-continued-pretraining",
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{
"stage": "joint-foundation-sft",
"step": 500,
"presentations": 32000,
"supervised_assistant_positions": 1027385,
"weight_sha256": "25b406a7b86e3e8ac4dd0b7f32e27590d9d11349a0148a7815c32304150ac725"
}
],
"sft_stages": 1,
"teachers_at_inference": false
}
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