Text Generation
Transformers
TensorBoard
Safetensors
English
qwen3
byte-level
pretraining
symbolic
text-generation-inference
Instructions to use dotlabs/void.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dotlabs/void.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dotlabs/void.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dotlabs/void.1") model = AutoModelForCausalLM.from_pretrained("dotlabs/void.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dotlabs/void.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dotlabs/void.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dotlabs/void.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dotlabs/void.1
- SGLang
How to use dotlabs/void.1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "dotlabs/void.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dotlabs/void.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "dotlabs/void.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dotlabs/void.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dotlabs/void.1 with Docker Model Runner:
docker model run hf.co/dotlabs/void.1
Qwen90M step 185000; complete resumable checkpoint
Browse files- complete.json +6 -6
- coverage.json +46 -46
- latest.json +1 -1
- model.safetensors +1 -1
- shard_scheduler.json +245 -245
- trainer.pt +2 -2
- training_config.json +1 -1
complete.json
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coverage.json
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latest.json
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model.safetensors
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shard_scheduler.json
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}
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],
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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
|
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@@ -155,413 +155,413 @@
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"shard_benchmark_scope": "Local packing/read measurements; upload capacity measured only on real data commits. Targets larger than available pages are sample-limited.",
|
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"estimated_fresh_generation_batch_seconds": 0.1434574083060457,
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"selected": {
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"prefetch_batches":
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"prefetch_capacity_limit": 512,
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"batch_memory_estimate_bytes": 688946,
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"producer_batch_seconds": 0.
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"producer_p95_seconds": 0.
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"gpu_update_seconds": 0.
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"producer_batches_per_second":
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"gpu_batches_per_second": 1.
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"producer_headroom":
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"sustainable": true,
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"buffer_seconds": 60.
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"buffer_drain_seconds": null,
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"shard_target_mib": 16,
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"upload_pages_per_commit":
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"max_pending_pages": 4096,
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"generation_bytes_per_second":
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"upload_bytes_per_second":
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"upload_sustainable": true,
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"logical_page_rows": 4096,
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"logical_cortex_page_rows": 128,
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"estimated_full_shards_per_hour":
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"selection_policy": "Smallest shard within 90% of measured local peak, enlarged to amortize 30 seconds of measured page production; bounded to 256 MiB."
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},
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"upload_measurements": [
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{
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"seconds": 1.
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},
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{
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"seconds": 1.0782650920009473,
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"bytes": 5102757,
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{
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{
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"seconds": 0.
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{
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{
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{
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"shards": 3
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{
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"pages": 34,
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{
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{
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{
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| 529 |
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"seconds": 1.
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"bytes":
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| 533 |
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| 535 |
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| 536 |
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| 537 |
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| 538 |
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| 539 |
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| 540 |
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| 541 |
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| 542 |
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| 543 |
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"seconds": 1.
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| 544 |
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"bytes":
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| 545 |
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| 547 |
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| 548 |
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| 549 |
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"seconds":
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"bytes":
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| 551 |
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"pages":
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"shards":
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| 553 |
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},
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| 554 |
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{
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"seconds": 1.6173429520022182,
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| 557 |
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| 558 |
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"shards": 2
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| 559 |
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},
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| 560 |
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{
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| 561 |
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"seconds": 1.705037297000672,
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| 562 |
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| 563 |
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| 564 |
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"shards": 3
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| 565 |
}
|
| 566 |
],
|
| 567 |
"cache_key": {
|
|
|
|
| 22 |
"tokens_per_second": 1062967.6168812835,
|
| 23 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 24 |
"estimated_pages_for_buffer": 1,
|
| 25 |
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"required_token_positions_per_second": 76375.43340313055,
|
| 26 |
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"required_pages_per_second_upper_estimate": 0.006620028995617115
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"source": "rewrite6",
|
|
|
|
| 36 |
"tokens_per_second": 1206672.263377472,
|
| 37 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 38 |
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|
| 39 |
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"required_token_positions_per_second": 9546.929175391318,
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| 40 |
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"required_pages_per_second_upper_estimate": 0.0038084127873748677
|
| 41 |
},
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| 42 |
{
|
| 43 |
"source": "openmath",
|
|
|
|
| 50 |
"tokens_per_second": 410042.7581134458,
|
| 51 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 52 |
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| 53 |
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"required_token_positions_per_second": 19093.858350782637,
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| 54 |
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"required_pages_per_second_upper_estimate": 0.004417028089023795
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| 55 |
},
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| 56 |
{
|
| 57 |
"source": "ultra_qa",
|
|
|
|
| 64 |
"tokens_per_second": 2163486.5624068812,
|
| 65 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 66 |
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| 67 |
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"required_token_positions_per_second": 28640.787526173957,
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| 68 |
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"required_pages_per_second_upper_estimate": 0.0017440736937450589
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| 69 |
},
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| 70 |
{
|
| 71 |
"source": "ultra_style",
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|
|
|
| 78 |
"tokens_per_second": 1546694.2737633225,
|
| 79 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 80 |
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| 81 |
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"required_token_positions_per_second": 28640.787526173957,
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| 82 |
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"required_pages_per_second_upper_estimate": 0.002204847106539067
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| 83 |
},
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| 84 |
{
|
| 85 |
"source": "cortex",
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|
|
|
| 92 |
"tokens_per_second": 1015177.8395898147,
|
| 93 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 94 |
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| 95 |
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"required_token_positions_per_second": 28640.787526173957,
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| 96 |
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"required_pages_per_second_upper_estimate": 0.3556227265253729
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| 97 |
}
|
| 98 |
],
|
| 99 |
"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
|
|
|
|
| 155 |
"shard_benchmark_scope": "Local packing/read measurements; upload capacity measured only on real data commits. Targets larger than available pages are sample-limited.",
|
| 156 |
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|
| 157 |
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| 158 |
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"prefetch_batches": 63,
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| 159 |
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| 160 |
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| 161 |
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| 162 |
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| 164 |
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| 166 |
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"producer_headroom": 1.9094667470536641,
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| 167 |
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| 168 |
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"buffer_seconds": 60.81620480610735,
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| 169 |
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|
| 170 |
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|
| 171 |
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"upload_pages_per_commit": 80,
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size
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training_config.json
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@@ -46,7 +46,7 @@
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