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/public/chat/protocol.json from OpenSML/Solstice-1: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
-
https://huggingface.co/OpenSML/Solstice-1/resolve/main/results/public/chat/protocol.json
- Command line
-
hf download hf://OpenSML/Solstice-1/results/public/chat/protocol.json
-
curl -L -o protocol.json https://huggingface.co/OpenSML/Solstice-1/resolve/main/results/public/chat/protocol.json
1.5 kB
| { | |
| "attention": "vanilla", | |
| "cases": "3fe1b15858329766a4fe532dad9d3aa1fb49f712b7b9f7443eb5049800b227ee", | |
| "ffn": "reference", | |
| "model_sha256": "7b2d8777040f05d8e0849acac4e45013b2de3d72145c363cd22825e80115c515", | |
| "precision": "FP32 base masters plus FP32 trained deltas", | |
| "prompt_truncation": false, | |
| "repetition_penalty": 1, | |
| "scoring": { | |
| "pretraining/chat_benchmark/panel.py": "70d78840147614727a34a775f669f9316c8d589721d41fdf2dd8c98deb2475bd", | |
| "pretraining/chat_benchmark/scoring.py": "e63a8715c302e2b59b0fadc3598e912d3f695fb9352b541e33dba3b046017c54", | |
| "pretraining/scripts/benchmark_best.py": "f3835c8cb0bdf580e4142a57ac3724c474dcc1d44aa01dbbd805c989a587c7c7", | |
| "pretraining/scripts/benchmark_ifeval.py": "10ae9d2c49236c377d6e9952674b14289f5f92c310c29cbe592a40c98a14c752", | |
| "pretraining/sft/data.py": "5c558b0cfd4d2061af144c3f295b7bec31d92cb4b0fb605f702972c373e2d1e8", | |
| "pretraining/sft/evaluate.py": "80624551512112850145ccb57d6435d7a1bae86d0b95f8f52d56b8cb6f102a76", | |
| "pretraining/sml_v1/model.py": "1eee6f8afe78767ff6b9c604ccf2328c98b24bc94dbad6291a0ad699dd7345fe", | |
| "pretraining/sml_v1/tokenization.py": "254e1f5d60cc4498a9f1209955752db28b91839f48d3121d674f3f5ac42e52e5" | |
| }, | |
| "system_prompt": null, | |
| "temperature": 0, | |
| "weight_provenance": { | |
| "precision": "FP32 pretrained masters plus FP32 LoRA and tied chat-token deltas", | |
| "resume": "Use the enclosing adapter bundle; this is an inference export", | |
| "trainable_parameters": 3785856 | |
| } | |
| } | |