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 native_utils.py from OpenSML/OpenSML-150M: direct link, hf CLI and curl.
- Browser
- Download file 252 Bytes
-
https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/native_utils.py
- Command line
-
hf download hf://OpenSML/OpenSML-150M/native_utils.py
-
curl -L -o native_utils.py https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/native_utils.py
252 Bytes
| import hashlib,json | |
| from pathlib import Path | |
| def read_json(p):return json.loads(Path(p).read_text()) | |
| def file_sha256(p): | |
| h=hashlib.sha256() | |
| with Path(p).open("rb") as f: | |
| for b in iter(lambda:f.read(4*1024**2),b""):h.update(b) | |
| return h.hexdigest() | |