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: 859 Bytes
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"model": "OpenSML-JSONLite-175M-Instruct",
"checkpoint": "main",
"date": "2026-10-20",
"playground60": {
"requests": 60,
"raw_text_token_stop_matches": 60,
"mismatches": [],
"complete_correct": 49,
"schema_valid": 60
},
"original1444": {
"requests": 1444,
"raw_text_token_stop_matches": 1444,
"mismatches": [],
"complete_correct": 1293,
"schema_valid": 1443
},
"weight_sha256": "25b406a7b86e3e8ac4dd0b7f32e27590d9d11349a0148a7815c32304150ac725",
"status": "passed",
"versions": {
"python": "3.11.13",
"mlx": "0.32.3",
"jsonschema": "4.26.0",
"tokenizers": "0.22.2"
},
"scope": "Live native packaged-source generation; text, output-token count and stop reason match saved selected-checkpoint generations for every tested request. No repair, assembly or constrained decoding."
}
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