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 corpus.json from OpenSML/OpenSML-Base-175M: direct link, hf CLI and curl.
- Browser
- Download file 2.17 kB
-
https://huggingface.co/OpenSML/OpenSML-Base-175M/resolve/main/corpus.json
- Command line
-
hf download hf://OpenSML/OpenSML-Base-175M/corpus.json
-
curl -L -o corpus.json https://huggingface.co/OpenSML/OpenSML-Base-175M/resolve/main/corpus.json
2.17 kB
| { | |
| "name": "research-20b-educational-python-v1", | |
| "seed": 7337, | |
| "shuffle_buffer": 10000, | |
| "min_chars": 200, | |
| "max_bytes": 200000, | |
| "max_rows_per_source": 50000000, | |
| "split_buckets": 10000, | |
| "sources": [ | |
| { | |
| "label": "web", | |
| "repo": "HuggingFaceTB/smollm-corpus", | |
| "config": "fineweb-edu-dedup", | |
| "revision": "3ba9d605774198c5868892d7a8deda78031a781f", | |
| "split": "train", | |
| "text_field": "text", | |
| "weight": 50, | |
| "license": "ODC-BY; preserve underlying source notices" | |
| }, | |
| { | |
| "label": "dclm", | |
| "repo": "HuggingFaceTB/dclm-edu", | |
| "revision": "dbad8ad71224482740cd9c9d353591adbf62fe04", | |
| "split": "train", | |
| "text_field": "text", | |
| "weight": 18, | |
| "min_score": { | |
| "field": "edu_int_score", | |
| "value": 3 | |
| }, | |
| "license": "CC-BY-4.0" | |
| }, | |
| { | |
| "label": "wiki", | |
| "repo": "HuggingFaceFW/finewiki", | |
| "config": "en", | |
| "revision": "8bd13e72e6a002407649b3e898535f42ceb1aeb9", | |
| "split": "train", | |
| "text_field": "text", | |
| "weight": 13, | |
| "license": "CC-BY-SA-4.0 / GFDL" | |
| }, | |
| { | |
| "label": "cosmopedia", | |
| "repo": "HuggingFaceTB/smollm-corpus", | |
| "config": "cosmopedia-v2", | |
| "revision": "3ba9d605774198c5868892d7a8deda78031a781f", | |
| "split": "train", | |
| "text_field": "text", | |
| "weight": 9, | |
| "format_contains": [ | |
| "textbook", | |
| "tutorial", | |
| "blog", | |
| "educational" | |
| ], | |
| "license": "ODC-BY; synthetic content; preserve provenance" | |
| }, | |
| { | |
| "label": "python", | |
| "repo": "Avelina/python-edu-cleaned", | |
| "config": "default", | |
| "revision": "3fedf55e02e72dd1fd68963f991f5e91adcb0f8d", | |
| "split": "train", | |
| "text_field": "text", | |
| "weight": 10, | |
| "min_score": { | |
| "field": "int_score", | |
| "value": 4 | |
| }, | |
| "upstream_repo": "HuggingFaceTB/smollm-corpus", | |
| "upstream_config": "python-edu", | |
| "upstream_revision": "3ba9d605774198c5868892d7a8deda78031a781f", | |
| "license": "Mirror metadata ODC-BY; original code remains subject to upstream repository licenses and The Stack v2 terms" | |
| } | |
| ] | |
| } | |