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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"source": "openmath",
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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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"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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"source": "cortex",
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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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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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"prefetch_batches": 64,
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"prefetch_capacity_limit": 512,
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"batch_memory_estimate_bytes": 720750,
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"producer_p95_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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"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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"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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"bytes": 12779121,
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"pages": 35,
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"shards": 4
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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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"source": "openmath",
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metrics/void-byte-v1/1791399463488568880-054ba646/train.jsonl
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{"step": 887300, "loss": 0.541535496711731, "lr": 3e-05, "grad_norm": 0.30549372524149293, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 129876072345, "tokens_per_second": 148892.38539015697, "data_wait_s": 1.0383000244473806e-05, "gpu_peak_gb": 89.37899398803711}
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{"step": 887400, "loss": 0.5499482750892639, "lr": 3e-05, "grad_norm": 0.33402700638135396, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 129890689891, "tokens_per_second": 152637.59663709343, "data_wait_s": 1.0358000054111471e-05, "gpu_peak_gb": 89.37899398803711}
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{"step": 887500, "loss": 0.4985582232475281, "lr": 3e-05, "grad_norm": 0.30761058333817004, "recovery_retries": 0, "loss_scale": 1.0, "microbatch": 90, "input_bytes_and_specials": 129905366636, "tokens_per_second": 153305.5516986935, "data_wait_s": 1.6431999938504305e-05, "gpu_peak_gb": 89.37899398803711}
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