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: 887 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 28 29 30 | {
"model_name": "OpenSML-150M",
"weights_sha256": "cbd3e3fb4ada74d7264371b79cee7598f513b7b950f1f141ef9f1a43bd2e1b4e",
"strict_loading": "passed",
"prompt": "Say hello in one sentence.",
"generation": {
"text": " I am a student.",
"token_ids": [
345,
768,
261,
2560,
17,
1
],
"stop_reason": "eos",
"prompt_tokens": 13,
"generated_tokens": 6
},
"precision": "FP32",
"attention": "vanilla",
"ffn": "reference",
"scope": "Short local loading/generation check; no benchmark rerun or cross-framework parity claim.",
"parameter_count": 150439168,
"tested_mlx_runtime": "0.32.3.dev20260920+7241f12",
"cached_full_prefix_argmax": "matched on checked prompt",
"context_overflow": "rejected",
"dependency_scope": "PyPI requirements are a baseline; smoke check used the recorded local development runtime."
}
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