Text Generation
MLX
Safetensors
English
base-model
pretrained
small-language-model
apple-silicon
from-scratch
research
Instructions to use OpenSML/OpenSML-Base-175M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenSML/OpenSML-Base-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-Base-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-Base-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-Base-175M" --prompt "Once upon a time"
- Atomic Chat
File size: 759 Bytes
8f2ec8e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"model": "OpenSML-Base-175M",
"stage": "Stage A pretraining continuation",
"step": 132005,
"tokens": 17300111360,
"model_file": "model.safetensors",
"weight_bytes": 351242342,
"weight_sha256": "5151567e266244c9e32fd6e01775192633abe227320269ecdefe28ce43e9ccc3",
"original_checkpoint_metadata_sha256": "15a0e3b511b97503a611c0da838a7fde1c06ebed34c7103f7cda1782cc6378a4",
"tokenizer_fingerprint": "1194292c6d906ac19e6f01c6ad2b42825143fad426a22aa8856d903776701ecb",
"initialization": "normal-0.02-residual-scaled-v1",
"post_training": false,
"descendant": "OpenSML/OpenSML-JSONLite-175M",
"model_weight_license": "not yet assigned",
"scope": "The pretrained ancestor; no QA/extraction SFT or JSON adapter is part of these weights."
}
|