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 670000; complete resumable checkpoint
Browse files- complete.json +6 -6
- coverage.json +49 -49
- latest.json +1 -1
- model.safetensors +1 -1
- shard_scheduler.json +251 -251
- trainer.pt +2 -2
- training_config.json +2 -2
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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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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-
"estimated_pages_for_buffer":
|
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-
"required_token_positions_per_second":
|
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-
"required_pages_per_second_upper_estimate": 0.
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}
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],
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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
|
|
@@ -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": 2.558590814491007,
|
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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": 720750,
|
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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":
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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":
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"bytes":
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"pages": 34,
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{
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{
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"seconds": 1.
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{
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"seconds": 1.
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{
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"bytes":
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"pages": 37,
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"shards": 2
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},
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"seconds": 1.0409215150011732,
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"bytes": 6007655,
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{
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{
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"pages":
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"seconds": 1.032158955000341,
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"bytes": 6117925,
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"seconds": 1.
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"seconds": 1.
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"seconds": 1.
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"seconds": 1.
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| 558 |
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"seconds":
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"bytes":
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| 563 |
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|
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|
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|
| 564 |
"shards": 1
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|
|
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|
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|
|
|
|
|
|
|
|
| 565 |
}
|
| 566 |
],
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| 567 |
"cache_key": {
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|
|
|
| 22 |
"tokens_per_second": 598315.063954628,
|
| 23 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 24 |
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| 25 |
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"required_token_positions_per_second": 74846.34535141522,
|
| 26 |
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"required_pages_per_second_upper_estimate": 0.0051780438388588255
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| 27 |
},
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| 28 |
{
|
| 29 |
"source": "rewrite6",
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|
|
|
| 36 |
"tokens_per_second": 8533.957213384612,
|
| 37 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 38 |
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| 39 |
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"required_token_positions_per_second": 9355.793168926903,
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| 40 |
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"required_pages_per_second_upper_estimate": 0.00391707522865193
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| 41 |
},
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| 42 |
{
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| 43 |
"source": "openmath",
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|
|
|
| 50 |
"tokens_per_second": 380144.35761751566,
|
| 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": 18711.586337853805,
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| 54 |
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"required_pages_per_second_upper_estimate": 0.003897129976459624
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| 55 |
},
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| 56 |
{
|
| 57 |
"source": "ultra_qa",
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|
|
|
| 64 |
"tokens_per_second": 43742.16556775435,
|
| 65 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 66 |
"estimated_pages_for_buffer": 1,
|
| 67 |
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"required_token_positions_per_second": 28067.379506780704,
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"required_pages_per_second_upper_estimate": 0.0016825965450244628
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},
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{
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| 71 |
"source": "ultra_style",
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|
|
|
| 78 |
"tokens_per_second": 22018.86276523721,
|
| 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": 28067.379506780704,
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"required_pages_per_second_upper_estimate": 0.0021821153012969137
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},
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{
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| 85 |
"source": "cortex",
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|
| 91 |
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|
| 92 |
"tokens_per_second": 910315.184299418,
|
| 93 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 94 |
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"estimated_pages_for_buffer": 22,
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| 95 |
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"required_token_positions_per_second": 28067.379506780704,
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"required_pages_per_second_upper_estimate": 0.36001820790883526
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| 97 |
}
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| 98 |
],
|
| 99 |
"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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|
|
|
| 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": 61,
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| 159 |
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@@ -53,8 +53,8 @@
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