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 checkpoint_provenance.json from OpenSML/OpenSML-150M: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/checkpoint_provenance.json
- Command line
-
hf download hf://OpenSML/OpenSML-150M/checkpoint_provenance.json
-
curl -L -o checkpoint_provenance.json https://huggingface.co/OpenSML/OpenSML-150M/resolve/main/checkpoint_provenance.json
1.46 kB
| { | |
| "model_name": "OpenSML-150M", | |
| "step": 768, | |
| "additional_updates": 256, | |
| "weights_sha256": "cbd3e3fb4ada74d7264371b79cee7598f513b7b950f1f141ef9f1a43bd2e1b4e", | |
| "source_metadata": { | |
| "additional_updates": 256, | |
| "automatic_promotion": false, | |
| "contract": "e7ad0c5c033aa86c115458bb4be6efe1d20e9d0c73aa8231480e16ff6f1f4fdd", | |
| "experimental": true, | |
| "source_bundle": "experiments/public_repair_384_v1/runs/sft/step_0000512_6972bbf9be08", | |
| "source_model_sha256": "465ce42aad2e7c765f169aa75aa4124ec03fec17b46e6f5eabf75c2c4a8d4772", | |
| "source_step": 512, | |
| "step": 768, | |
| "tokenizer": "1194292c6d906ac19e6f01c6ad2b42825143fad426a22aa8856d903776701ecb", | |
| "training_format": "plain-user-assistant-eos-v1" | |
| }, | |
| "source_manifest": { | |
| "created_ns": 1790893470379935000, | |
| "files": { | |
| "model.safetensors": { | |
| "bytes": 601772434, | |
| "sha256": "cbd3e3fb4ada74d7264371b79cee7598f513b7b950f1f141ef9f1a43bd2e1b4e" | |
| }, | |
| "model.safetensors.json": { | |
| "bytes": 525, | |
| "sha256": "7c0e307d7bc1973a1d5b8c683b2911c4dd2fead65d49226cdce53d6dfe098908" | |
| }, | |
| "model.safetensors.optimizer.safetensors": { | |
| "bytes": 1805319748, | |
| "sha256": "a23dd9b5ca2af99ac3a7648a7b9a70acb04c183aacbea71fdaabd2a1fdc2c720" | |
| } | |
| }, | |
| "format": "sml-pretrain-bundle-v1", | |
| "step": 768 | |
| }, | |
| "export": "Exact unchanged FP32 weights; optimizer tensors excluded. Native reference MLX inference." | |
| } | |