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 SHA256SUMS from OpenSML/OpenSML-Base-175M: direct link, hf CLI and curl.
- Browser
- Download file 1.87 kB
-
https://huggingface.co/OpenSML/OpenSML-Base-175M/resolve/main/SHA256SUMS
- Command line
-
hf download hf://OpenSML/OpenSML-Base-175M/SHA256SUMS
-
curl -L -o SHA256SUMS https://huggingface.co/OpenSML/OpenSML-Base-175M/resolve/main/SHA256SUMS
1.87 kB
| 59ee3f5b0b4e357e34a390a72febcc13eac5493fb4a4c2d7ecdf6ba5be324ff2 CITATION.cff | |
| 9c6d4c439acf90f9187ba6cf11f070afb3ea1515d488b52c381adf9356a1d613 EVALUATION_RESULTS.json | |
| c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4 LICENSE | |
| ca872a6a670973217f0c38f0442187a8cbd3b2799ed96a95f3d94d2aa50ed52c PACKAGING_VERIFICATION.json | |
| 14819fc05509546bb8218153cf8494c6a3c4e2e4744ce504bdc21ef0d7d1ed59 PRETRAINING_SAMPLES.json | |
| 6552351fda98537830ca166261eaa69b7797281d37d50b41fcaae0dd388cdf14 README.md | |
| e7463ee8f054f2cd7ff9f8d64b1fb8a8b593052f3e91908231a7e3f38c81c5f1 RUN_GUIDE.md | |
| c3bd058575a0313f716dabb8de8a3d7b729ff413dae5f74b06525c0737dcfac3 SOURCE_FILES.json | |
| 3f1a1bf4d3f6c41c1c607cc32fd4ae10def8b3466e0f7d09b99030fe32260259 TECHNICAL_REPORT.md | |
| 3235c99f09bfab6e5ee95e64a382a4975978ec7f8988d33a71d4aaeb0215bf15 THIRD_PARTY_NOTICES.md | |
| 15a0e3b511b97503a611c0da838a7fde1c06ebed34c7103f7cda1782cc6378a4 checkpoint_metadata.json | |
| 22eaa19ba2346558711ff5245b4a1e581a431b7b1c5df3c2342f52407ebbc74a checkpoint_provenance.json | |
| 6092bad50bb78f61bf3f79c26f57c68b8cb9b47644de5290789f11fe0c66ea64 config.json | |
| d31deb465fd3ba8dcd4590ecf9f01375246784df91217442ae5fcbf0f7ab252b corpus.json | |
| e478df98a1ac6d0b9d090fdf5d593ad5ad651eaaf67bfff30a1fb313c926d321 inference.py | |
| 5151567e266244c9e32fd6e01775192633abe227320269ecdefe28ce43e9ccc3 model.safetensors | |
| 14f85f0ff677d35a6fb34f118f2dd21a3fdd504fff27c163860ab919b21e0e98 native_bundle.zip | |
| 641fe860d6fd24547fe4d15bd852190f00ddec31bd74eb7ca9eeae74031c2f0c pretraining-training-loss.png | |
| cfeac63c4d14d73a7b247bf9e39fd0b4642754aa4b69e7ea6cf94e79a594e3d8 requirements.txt | |
| e095f0413a11fa91fbb58f37d00e4c6bcda59beb89b28f6f680db1436a7e8891 tokenizer.json | |
| 619f500553367eb76f5be00a98625130ec857b9c367561334984096c029ba2a9 tokenizer_config.json | |
| 1194292c6d906ac19e6f01c6ad2b42825143fad426a22aa8856d903776701ecb tokenizer_manifest.json | |