Text Generation
MLX
Safetensors
English
small-language-model
from-scratch
conversational
apple-silicon
research
Instructions to use OpenSML/Solstice-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenSML/Solstice-1 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OpenSML/Solstice-1") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use OpenSML/Solstice-1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OpenSML/Solstice-1"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OpenSML/Solstice-1" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenSML/Solstice-1", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Download evaluation/vendor/ifeval/source.json from OpenSML/Solstice-1: direct link, hf CLI and curl.
- Browser
- Download file 649 Bytes
-
https://huggingface.co/OpenSML/Solstice-1/resolve/main/evaluation/vendor/ifeval/source.json
- Command line
-
hf download hf://OpenSML/Solstice-1/evaluation/vendor/ifeval/source.json
-
curl -L -o source.json https://huggingface.co/OpenSML/Solstice-1/resolve/main/evaluation/vendor/ifeval/source.json
649 Bytes
| { | |
| "format": "opensml-ifeval-runtime-v1", | |
| "dataset": "google/IFEval", | |
| "dataset_revision": "966cd89545d6b6acfd7638bc708b98261ca58e84", | |
| "google_research_commit": "83f1e7b59e762648c953de751d31f75720683622", | |
| "evaluator_blob_sha1": { | |
| "evaluation_lib.py": "c87422822f9e1565ca7574ac0b3cee5389c9f44e", | |
| "instructions.py": "0007359bd37c7b9c1d9557f8937cb6586d2788a7", | |
| "instructions_registry.py": "6b42adcc4c8cc827810ab3efa8a06f02bb48c397", | |
| "instructions_util.py": "d50702400738df4378fe286c0090a416f9a172fa" | |
| }, | |
| "provenance": "Local evaluator files byte-match the pinned official Google Research files.", | |
| "expected_prompts": 541 | |
| } | |