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 scripts/materialize_training_snapshot.py from OpenSML/Solstice-1: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
-
https://huggingface.co/OpenSML/Solstice-1/resolve/main/scripts/materialize_training_snapshot.py
- Command line
-
hf download hf://OpenSML/Solstice-1/scripts/materialize_training_snapshot.py
-
curl -L -o materialize_training_snapshot.py https://huggingface.co/OpenSML/Solstice-1/resolve/main/scripts/materialize_training_snapshot.py
1.82 kB
| #!/usr/bin/env python3 | |
| """Restore an archived training source tree into a new directory.""" | |
| import argparse | |
| import hashlib | |
| import json | |
| from pathlib import Path | |
| ROOT = Path(__file__).resolve().parents[1] | |
| def main(): | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument('--list', action='store_true') | |
| parser.add_argument('--snapshot') | |
| parser.add_argument('--output', type=Path) | |
| args = parser.parse_args() | |
| index = json.loads((ROOT/'training/SNAPSHOTS.json').read_text()) | |
| if args.list: | |
| print(json.dumps(index, indent=2)); return | |
| if args.snapshot not in index or args.output is None: | |
| parser.error('Choose --snapshot from --list and supply a new --output directory.') | |
| output = args.output.resolve() | |
| if output.exists(): | |
| parser.error('Output already exists; choose a new directory. No files were changed.') | |
| manifest = json.loads((ROOT/index[args.snapshot]['snapshot']).read_text()) | |
| checked = [] | |
| for name, record in manifest['files'].items(): | |
| destination = (output/name).resolve() | |
| source = (ROOT/record['file']).resolve() | |
| if not destination.is_relative_to(output) or not source.is_relative_to(ROOT): | |
| raise ValueError('Invalid snapshot path') | |
| data = source.read_bytes() | |
| if hashlib.sha256(data).hexdigest() != record['packaged_sha256']: | |
| raise ValueError(f'Snapshot integrity mismatch: {name}') | |
| checked.append((destination, data)) | |
| output.mkdir(parents=True, exist_ok=False) | |
| for destination, data in checked: | |
| destination.parent.mkdir(parents=True, exist_ok=True) | |
| destination.write_bytes(data) | |
| print(json.dumps({'snapshot': args.snapshot, 'files': len(checked), 'output': str(output)}, indent=2)) | |
| if __name__ == '__main__': | |
| main() | |