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 610000; complete resumable checkpoint
Browse files- complete.json +5 -5
- coverage.json +50 -50
- latest.json +1 -1
- model.safetensors +1 -1
- shard_scheduler.json +269 -23
- trainer.pt +2 -2
complete.json
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coverage.json
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latest.json
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{"step":
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{"step": 610000, "files": {"config.json": "cbc72137da3dae65085a2a8c39ad7634d16bf92d785ca00d283ee82fe32b37e7", "generation_config.json": "54ed0d19f6226888c91157964ede8a892bd5ae175bfad85336152b3d9b6cd7c4", "model.safetensors": "8b25da00cb0b2a963e4cb081606d08666f7bba3d0bf0aee4e1acf0666e72cf45", "tokenization_byte.py": "279b9990f77b1eaedc7c1184fe13d60273a713ead856200f1fb9ae323c7b1e03", "tokenizer_config.json": "0ecbd4fd39e35125800d806af981ba0cacd8a7ba5f02c9e729c4ecc54af6d300", "special_tokens_map.json": "20d4b94d0e657c3f44d9123522c106e7315b6c1a1b6d259478f17bd2918b1263", "byte_vocab.json": "57a1cd2e70dd5b7cc6fd2eaa12ebfcd173c1620fa5d45c2c6282511c29b6a5f6", "trainer.pt": "478e3feddd5e715e333af4d01c7c276cb17b2faf46c01877e15a16c415e9bb60", "training_config.json": "6c3c3ccff89dabb5098a22bc207c2f3abaee8b3bffec3f6cf6ad361a63b1a7fb", "performance.json": "539995c1e4203016bfc6259363445c7c25dfb51604d74cd18d2cc7ad51bfb71e", "shard_scheduler.json": "d2dd9104d6ecd28878b172aff3036091ad951fcff01930fc538e5377b692dfb4", "coverage.json": "fdc409abc87a90ebef70a4935f32045cc301b48f97054e4d2a9d47db721dea80", "README.md": "9ce0f9efacbc46177a7e818b6aed274d84e7145ff944b5c7d6ecd5d33b9d4419"}, "signature": "33a459803c8cffa8929a90def998b4ad2c7c52fd1245365a9ee946b5552d845a", "folder": "step-000000610000"}
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model.safetensors
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oid sha256:
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shard_scheduler.json
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|
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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.",
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@@ -158,24 +158,24 @@
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"prefetch_batches": 63,
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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.
|
| 162 |
-
"producer_p95_seconds": 0.
|
| 163 |
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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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"bytes": 6002577,
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"pages": 28,
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"shards": 2
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}
|
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],
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| 22 |
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| 23 |
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| 24 |
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{
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"source": "rewrite6",
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| 36 |
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"source": "openmath",
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"source": "ultra_qa",
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| 64 |
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"source": "ultra_style",
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| 78 |
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|
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size
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size 721778367
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