Text Generation
MLX
Safetensors
English
pretraining
from-scratch
small-language-model
post-training
silicon
Instructions to use OpenSML/OpenSML-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenSML/OpenSML-150M 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-150M") 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-150M 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-150M" --prompt "Once upon a time"
- Atomic Chat
File size: 790 Bytes
8662ab2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"repository": "wzebrowski/OpenSML-150M",
"revision": "27ba9cd101492c0dbfeb6cea0147e429a86d8648",
"download": "Fresh force-download of 17 release files with no authentication",
"hashes": "All SHA256SUMS entries verified",
"working_directory": "/private/tmp (outside original project)",
"environment": "Separate temporary virtual environment; published requirements installed from PyPI",
"mlx": "0.32.2",
"mlx-metal": "0.32.2",
"tokenizers": "0.22.2",
"generation": {
"text": " I am a student.",
"token_ids": [
345,
768,
261,
2560,
17,
1
],
"stop_reason": "eos",
"prompt_tokens": 13,
"generated_tokens": 6
},
"scope": "Strict loading and short inference test; no benchmark rerun or broad quality claim."
}
|