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
Download TRAINING_RECORDS_MANIFEST.json from OpenSML/OpenSML-150M: direct link, hf CLI and curl.
- Browser
- Download file 38.7 kB
-
https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/TRAINING_RECORDS_MANIFEST.json
- Command line
-
hf download hf://OpenSML/OpenSML-150M/TRAINING_RECORDS_MANIFEST.json
-
curl -L -o TRAINING_RECORDS_MANIFEST.json https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/TRAINING_RECORDS_MANIFEST.json
38.7 kB
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