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: 616 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 | {
"model_name": "OpenSML-150M",
"format": "opensml-native-mlx-v1",
"model": {
"attention_impl": "vanilla",
"bias": false,
"ce_impl": "reference",
"d_model": 768,
"ffn_impl": "reference",
"loss_dtype": "float32",
"max_seq_len": 2048,
"mlp_multiple_of": 256,
"mlp_ratio": 4.0,
"n_heads": 12,
"n_kv_heads": 4,
"n_layers": 20,
"qk_norm": true,
"vocab_size": 32000
},
"training_format": "plain-user-assistant-eos-v1",
"dtype": "float32",
"context_length": 2048,
"weights_sha256": "cbd3e3fb4ada74d7264371b79cee7598f513b7b950f1f141ef9f1a43bd2e1b4e"
}
|