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 tokenizer_config.json from OpenSML/OpenSML-150M: direct link, hf CLI and curl.
- Browser
- Download file 200 Bytes
-
https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://OpenSML/OpenSML-150M/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/tokenizer_config.json
200 Bytes
| { | |
| "add_bos_token": false, | |
| "add_eos_token": false, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|doc_end|>", | |
| "pad_token": "<|pad|>", | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |