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: 913 Bytes
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"checkpoint": "step_0000128_5937a6def881",
"compute_precision": "float32",
"context_limit": 2048,
"format": "opensml-native-chat-v1",
"license": "apache-2.0",
"model": {
"attention_impl": "vanilla",
"bias": false,
"ce_impl": "reference",
"d_model": 576,
"ffn_impl": "reference",
"loss_dtype": "float32",
"max_seq_len": 2048,
"mlp_multiple_of": 256,
"mlp_ratio": 4.0,
"n_heads": 9,
"n_kv_heads": 3,
"n_layers": 44,
"qk_norm": true,
"vocab_size": 32000
},
"model_id": "OpenSML/Solstice-1",
"model_name": "Solstice-1",
"organization": "OpenSML",
"parameter_label": "175M",
"parameters": 174202432,
"serialization": "explicit-ids-assistant-mask-v1",
"tokenizer_fingerprint": "1194292c6d906ac19e6f01c6ad2b42825143fad426a22aa8856d903776701ecb",
"weight_sha256": "7b2d8777040f05d8e0849acac4e45013b2de3d72145c363cd22825e80115c515"
}
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