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
File size: 723 Bytes
7bcd331 f3676c8 7bcd331 f3676c8 7bcd331 f3676c8 7bcd331 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"model": "Solstice-1",
"organization": "OpenSML",
"parameter_label": "175M",
"license": "Apache-2.0",
"release_status": "public research release",
"repositories": {
"github": {
"url": "https://github.com/williamzebrowskI/Solstice-1",
"visibility": "public",
"role": "Native inference code, documentation, training provenance, evaluation evidence"
},
"huggingface": {
"url": "https://huggingface.co/OpenSML/Solstice-1",
"visibility": "public",
"role": "Complete runnable package, including merged FP32 weights and tokenizer"
}
},
"weight_file": "model.safetensors",
"weight_sha256": "7b2d8777040f05d8e0849acac4e45013b2de3d72145c363cd22825e80115c515"
}
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