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: 6,968 Bytes
7bcd331 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | [
{
"assistant_tokens_recorded": 4440608,
"bundle": "step_0002000_f3d1ffd185f9",
"input_tokens_recorded": 29271514,
"manifest_sha256": "f9273450799963b2fd8e61a5e9cb6892be7ba4a2308849623e288302a687a1f7",
"optimizer": "Fresh AdamW moments at each SFT stage",
"parent_manifest": null,
"plan": "configs/sft1/plan.json",
"prose_replay": {
"presentations": 32000,
"tokens": 16384000
},
"source_pretraining_tokens": 42001367040,
"stage": "sft1",
"steps": 2000,
"supervised": {
"components": {
"constraints": 12400,
"dialogue": 16000,
"general": 9600,
"grounded": 22000,
"knowledge": 8000,
"rewrite": 2000,
"skills": 24000,
"social": 2000
},
"maximum_serialized_tokens": 1385,
"presentations": 96000,
"subsets": {
"sciq": 8000,
"smol": 12000,
"smol_constraints": 4000,
"smol_rewrite": 2000,
"social": 2000,
"squad": 22000,
"verified_constraints": 8400,
"verified_dialogue": 13600,
"verified_skills": 24000
},
"unique_rows": 48000
},
"training_sources": [
{
"label": "smol",
"license": "apache-2.0",
"parquet_urls": [
"https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk/resolve/f73fe857d519ff6ac5af2ea67c4d3834da7b8bcc/data/train-00000-of-00004.parquet",
"https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk/resolve/f73fe857d519ff6ac5af2ea67c4d3834da7b8bcc/data/train-00001-of-00004.parquet",
"https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk/resolve/f73fe857d519ff6ac5af2ea67c4d3834da7b8bcc/data/train-00002-of-00004.parquet",
"https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk/resolve/f73fe857d519ff6ac5af2ea67c4d3834da7b8bcc/data/train-00003-of-00004.parquet"
],
"repo": "HuggingFaceTB/smol-smoltalk",
"revision": "f73fe857d519ff6ac5af2ea67c4d3834da7b8bcc",
"split": "train"
},
{
"label": "social",
"license": "see upstream component license",
"parquet_urls": [
"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/resolve/5feaf2fd3ffca7c237fc38d1861bc30365d48ffa/data/everyday-conversations/train-00000-of-00001.parquet"
],
"repo": "HuggingFaceTB/smoltalk",
"revision": "5feaf2fd3ffca7c237fc38d1861bc30365d48ffa",
"split": "train"
},
{
"label": "squad",
"license": [
"cc-by-sa-4.0"
],
"parquet_urls": [
"https://huggingface.co/datasets/rajpurkar/squad_v2/resolve/3ffb306f725f7d2ce8394bc1873b24868140c412/squad_v2/train-00000-of-00001.parquet"
],
"repo": "rajpurkar/squad_v2",
"revision": "3ffb306f725f7d2ce8394bc1873b24868140c412",
"split": "train"
},
{
"label": "sciq",
"license": "cc-by-nc-3.0",
"parquet_urls": [
"https://huggingface.co/datasets/allenai/sciq/resolve/2c94ad3e1aafab77146f384e23536f97a4849815/data/train-00000-of-00001.parquet"
],
"repo": "allenai/sciq",
"revision": "2c94ad3e1aafab77146f384e23536f97a4849815",
"split": "train"
}
]
},
{
"actual_sources_note": "Fresh tasks are authored/computed; replay is selected from stage-1 training. Inherited plan.sources and corpus manifest describe broader/stale configuration, not additional fetched sources.",
"assistant_tokens_recorded": 279606,
"bundle": "step_0000256_6074b98fe66b",
"fresh": {
"components": {
"constraints": 1638,
"dialogue": 410,
"grounded": 410,
"skills": 1638
},
"maximum_serialized_tokens": 115,
"presentations": 4096,
"subsets": {
"unspecified": 4096
},
"unique_rows": 4096
},
"fresh_file_sha256": "0a90cbb82b2587f20026dd21c9d1697ab37016c7714e19caade3ce4c94856dcc",
"input_tokens_recorded": 816428,
"manifest_sha256": "dcab4ad8e4484ad1cc4b32ffbc6352b6a6ff9ea0a4ddccdb8d367d23db098a3f",
"optimizer": "Fresh AdamW moments at each SFT stage",
"parent_manifest": "f9273450799963b2fd8e61a5e9cb6892be7ba4a2308849623e288302a687a1f7",
"plan": "configs/sft2/plan.json",
"replay": {
"components": {
"constraints": 512,
"dialogue": 512,
"general": 768,
"grounded": 512,
"knowledge": 768,
"rewrite": 256,
"skills": 512,
"social": 256
},
"maximum_serialized_tokens": 512,
"presentations": 4096,
"subsets": {
"sciq": 768,
"smol": 781,
"smol_constraints": 162,
"smol_rewrite": 256,
"social": 256,
"squad": 512,
"verified_constraints": 350,
"verified_dialogue": 499,
"verified_skills": 512
},
"unique_rows": 4096
},
"replay_file_sha256": "3b7897ad24c215c309b1a693dc6b3606783c678e82f7d595bdc3f9f97d8797ab",
"source_pretraining_tokens": 42001367040,
"stage": "sft2",
"steps": 256
},
{
"actual_sources_note": "Fresh tasks are authored/computed; replay is selected from stage-1 training. Inherited plan.sources and corpus manifest describe broader/stale configuration, not additional fetched sources.",
"assistant_tokens_recorded": 120629,
"bundle": "step_0000128_5937a6def881",
"fresh": {
"components": {
"abstention": 188,
"dialogue": 306,
"grounded": 324,
"reasoning": 1230
},
"maximum_serialized_tokens": 137,
"presentations": 2048,
"subsets": {
"unspecified": 2048
},
"unique_rows": 2048
},
"fresh_file_sha256": "c32660b92756608b1c949730c61595dc78d29b0afb3695caf6cfc8397d4acbf5",
"input_tokens_recorded": 411448,
"manifest_sha256": "2118c7446c412a4ee7a4b7a356a211ad91574983e213da45980707b5fa1ca124",
"optimizer": "Fresh AdamW moments at each SFT stage",
"parent_manifest": "dcab4ad8e4484ad1cc4b32ffbc6352b6a6ff9ea0a4ddccdb8d367d23db098a3f",
"plan": "configs/sft3/plan.json",
"replay": {
"components": {
"constraints": 246,
"dialogue": 317,
"general": 352,
"grounded": 226,
"knowledge": 413,
"rewrite": 130,
"skills": 254,
"social": 110
},
"maximum_serialized_tokens": 512,
"presentations": 2048,
"subsets": {
"sciq": 413,
"smol": 365,
"smol_constraints": 73,
"smol_rewrite": 130,
"social": 110,
"squad": 226,
"verified_constraints": 173,
"verified_dialogue": 304,
"verified_skills": 254
},
"unique_rows": 2048
},
"replay_file_sha256": "3b7897ad24c215c309b1a693dc6b3606783c678e82f7d595bdc3f9f97d8797ab",
"source_pretraining_tokens": 42001367040,
"stage": "sft3",
"steps": 128
}
]
|