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
Download EVALUATION_RESULTS.json from OpenSML/OpenSML-Base-175M: direct link, hf CLI and curl.
- Browser
- Download file 980 Bytes
-
https://huggingface.co/OpenSML/OpenSML-Base-175M/resolve/main/EVALUATION_RESULTS.json
- Command line
-
hf download hf://OpenSML/OpenSML-Base-175M/EVALUATION_RESULTS.json
-
curl -L -o EVALUATION_RESULTS.json https://huggingface.co/OpenSML/OpenSML-Base-175M/resolve/main/EVALUATION_RESULTS.json
980 Bytes
| { | |
| "model": "OpenSML-Base-175M", | |
| "pretraining_step": 132005, | |
| "processed_token_positions": 17300111360, | |
| "weight_sha256": "5151567e266244c9e32fd6e01775192633abe227320269ecdefe28ce43e9ccc3", | |
| "validation": { | |
| "val_loss": 2.5563359551876785, | |
| "val_ppl": 12.888506628383553, | |
| "source_losses": { | |
| "web": 2.941684268414974, | |
| "dclm": 2.9943208396434784, | |
| "wiki": 1.987750370055437, | |
| "cosmopedia": 1.7873524278402328, | |
| "python": 1.2724680323153734 | |
| }, | |
| "validation_fingerprint": "4ddd461c131d4d27d07bb07f5aab3fcb5a00d4b1202066e3d9b6ee6b3129087b", | |
| "batches_per_source": 32, | |
| "batch_size": 2, | |
| "sequence_length": 2048, | |
| "sources": 5, | |
| "nominal_token_positions": 655360, | |
| "scope": "Historical fixed document validation, mixture-weighted loss. Not a downstream benchmark or a fresh test." | |
| }, | |
| "downstream_benchmarks": {}, | |
| "scope": "No JSONLite post-training or extraction metrics are attributed to these base weights." | |
| } | |