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
Update TensorBoard training metrics
Browse files
benchmark_cache/shard_scheduler.json
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"tokens_per_second": 1062967.6168812835,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"source": "rewrite6",
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"tokens_per_second": 1206672.263377472,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"required_token_positions_per_second":
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"source": "openmath",
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"tokens_per_second": 410042.7581134458,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"required_token_positions_per_second":
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{
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"source": "ultra_qa",
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"tokens_per_second": 2163486.5624068812,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"source": "ultra_style",
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"tokens_per_second": 1546694.2737633225,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"required_token_positions_per_second":
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"source": "cortex",
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"read_seconds": 0.042780299000014566,
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"tokens_per_second": 1015177.8395898147,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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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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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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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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"producer_batch_seconds": 0.
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"producer_p95_seconds": 0.
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"gpu_update_seconds":
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"producer_batches_per_second":
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"gpu_batches_per_second":
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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": 32,
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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": 494707,
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"pages": 15,
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"shards": 1
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"cache_key": {
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"source": "rewrite6",
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"source": "openmath",
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"source": "ultra_qa",
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"upload_pages_per_commit": 95,
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"cache_key": {
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metrics/void-byte-v1/1790390190216476996-4d8dc439/train.jsonl
CHANGED
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{"step": 100, "loss": 2.8035809621214867, "lr": 2.9999999999999997e-05, "grad_norm": 1.586405728010338, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 86, "input_bytes_and_specials": 14758037, "tokens_per_second": 46373.26368537362, "data_wait_s": 8.257999979832675e-06, "gpu_peak_gb": 85.43514251708984}
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{"step": 100, "loss": 2.8035809621214867, "lr": 2.9999999999999997e-05, "grad_norm": 1.586405728010338, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 86, "input_bytes_and_specials": 14758037, "tokens_per_second": 46373.26368537362, "data_wait_s": 8.257999979832675e-06, "gpu_peak_gb": 85.43514251708984}
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{"step": 200, "loss": 2.3905331194400787, "lr": 5.9999999999999995e-05, "grad_norm": 1.9926730765732787, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 86, "input_bytes_and_specials": 29475318, "tokens_per_second": 151060.5326281325, "data_wait_s": 9.137000006376184e-06, "gpu_peak_gb": 85.43514251708984}
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runs/void-byte-v1/events.out.tfevents.1790390190.c8125a87-1345-4bfd-b42f-0e32ed8b7bb7-6w295.451.0.1790390190216476996-4d8dc439
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