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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"source": "rewrite6",
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"tokens_per_second": 8533.957213384612,
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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": 380144.35761751566,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"source": "ultra_qa",
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"tokens_per_second": 43742.16556775435,
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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_style",
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"tokens_per_second": 22018.86276523721,
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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": "cortex",
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"read_seconds": 0.04061180899998362,
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"tokens_per_second": 910315.184299418,
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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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],
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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": 2.558590814491007,
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"selected": {
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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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"sustainable": true,
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"buffer_seconds": 60.
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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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"upload_measurements": [
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"seconds": 1.0340174030006892,
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"bytes": 2678432,
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"pages": 36,
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"shards": 2
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"seconds": 1.1785862379992977,
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"bytes": 9722174,
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"bytes": 2603502,
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"cache_key": {
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"source": "openmath",
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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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metrics/void-byte-v1/1791209746770022608-f3fe5fb7/train.jsonl
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{"step": 732500, "loss": 0.5607808828353882, "lr": 5e-05, "grad_norm": 0.2701238554798232, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 107217652722, "tokens_per_second": 153406.1708068083, "data_wait_s": 1.7020000086631626e-05, "gpu_peak_gb": 89.37899398803711}
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{"step": 732600, "loss": 0.5459712743759155, "lr": 5e-05, "grad_norm": 0.22150784871969925, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 107232409461, "tokens_per_second": 150610.48302994028, "data_wait_s": 1.8864000594476238e-05, "gpu_peak_gb": 89.37899398803711}
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{"step": 732700, "loss": 0.5102741122245789, "lr": 5e-05, "grad_norm": 0.21796404490415877, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 107247016435, "tokens_per_second": 149856.62709858778, "data_wait_s": 1.8905999240814708e-05, "gpu_peak_gb": 89.37899398803711}
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{"step": 732500, "loss": 0.5607808828353882, "lr": 5e-05, "grad_norm": 0.2701238554798232, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 107217652722, "tokens_per_second": 153406.1708068083, "data_wait_s": 1.7020000086631626e-05, "gpu_peak_gb": 89.37899398803711}
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runs/void-byte-v1/events.out.tfevents.1791209746.ebd20fd8-0561-42d8-bf2b-56d08e7106ef-6qqk2.329.0.1791209746770022608-f3fe5fb7
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