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 results/selection/batch_order_audit.json from OpenSML/Solstice-1: direct link, hf CLI and curl.
- Browser
- Download file 1.72 kB
-
https://huggingface.co/OpenSML/Solstice-1/resolve/main/results/selection/batch_order_audit.json
- Command line
-
hf download hf://OpenSML/Solstice-1/results/selection/batch_order_audit.json
-
curl -L -o batch_order_audit.json https://huggingface.co/OpenSML/Solstice-1/resolve/main/results/selection/batch_order_audit.json
1.72 kB
| { | |
| "actual_order_hash": "5e4cc1c34f86ab5228a3b82a5c1e7d50645ec80db40b40c9febef8a943740617", | |
| "actual_worker_matches": true, | |
| "clarification": "Preflight metadata now uses the same sorted-key serialization as the stored worker plans; actual training remains unchanged.", | |
| "matched_trial_1_and_2": true, | |
| "previous_preflight_orders": [ | |
| { | |
| "attempt": 1, | |
| "combined_trial_uses_provisional_balanced_mixture": false, | |
| "fresh_selected": 4096, | |
| "order_hash": "39767fb7a16b89e5d484ef28024da3f82484aa6edf6939c9203e5bd2d8380efa", | |
| "replay_selected": 4096, | |
| "steps": 256 | |
| }, | |
| { | |
| "attempt": 2, | |
| "combined_trial_uses_provisional_balanced_mixture": false, | |
| "fresh_selected": 4096, | |
| "order_hash": "39767fb7a16b89e5d484ef28024da3f82484aa6edf6939c9203e5bd2d8380efa", | |
| "replay_selected": 4096, | |
| "steps": 256 | |
| }, | |
| { | |
| "attempt": 3, | |
| "combined_trial_uses_provisional_balanced_mixture": false, | |
| "fresh_selected": 4096, | |
| "order_hash": "3a5cd6484ce0394014a6313326e9006fc33db64a419679fb1246536af5b12039", | |
| "replay_selected": 4096, | |
| "steps": 256 | |
| }, | |
| { | |
| "attempt": 4, | |
| "combined_trial_uses_provisional_balanced_mixture": false, | |
| "fresh_selected": 4096, | |
| "order_hash": "543c0e7f502549e0c45a3c6accef2c4b728f46f2d31f5d387e027dbb2a68fd5a", | |
| "replay_selected": 4096, | |
| "steps": 256 | |
| }, | |
| { | |
| "attempt": 5, | |
| "combined_trial_uses_provisional_balanced_mixture": true, | |
| "fresh_selected": 4096, | |
| "order_hash": "6e772977dfccd8adf2caf2b6e647b40a04ac3202ea764b28b61f12386cd1f534", | |
| "replay_selected": 4096, | |
| "steps": 256 | |
| } | |
| ], | |
| "training_runtime_or_data_changed": false | |
| } | |