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/performance.json
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"selected": {
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"input_tokens_per_second":
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},
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"measurements": [
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{
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"selected": {
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"microbatch": 90,
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"mode": "compiled",
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"microbatch_seconds": 0.9390849369999614,
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"peak_gb": 89.37790203094482,
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"fits_budget": true,
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"estimated_update_seconds": 0.9390849369999614,
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"input_tokens_per_second": 196276.1756022156
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"measurements": [
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{
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benchmark_cache/shard_scheduler.json
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"tokens_per_second": 598315.063954628,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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{
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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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"required_token_positions_per_second":
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{
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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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| 463 |
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"seconds": 1.489227063000726,
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| 469 |
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"seconds": 0.9323788800029433,
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"seconds": 1.5299789529963164,
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"seconds": 1.7032136210036697,
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| 567 |
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| 568 |
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| 569 |
"kind": "data",
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|
@@ -606,7 +221,7 @@
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| 606 |
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| 607 |
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| 608 |
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| 609 |
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| 610 |
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0.
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| 611 |
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| 612 |
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| 22 |
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| 23 |
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| 24 |
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| 27 |
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| 28 |
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| 29 |
"source": "rewrite6",
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| 36 |
"tokens_per_second": 8533.957213384612,
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| 37 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 38 |
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| 41 |
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| 42 |
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| 43 |
"source": "openmath",
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| 50 |
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| 51 |
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| 52 |
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| 55 |
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| 56 |
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| 57 |
"source": "ultra_qa",
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| 64 |
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| 65 |
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| 66 |
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| 69 |
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| 70 |
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| 71 |
"source": "ultra_style",
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| 78 |
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|
| 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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| 83 |
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| 84 |
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| 85 |
"source": "cortex",
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|
| 92 |
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|
| 93 |
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| 94 |
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| 95 |
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|
| 96 |
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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": 64,
|
| 159 |
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|
| 160 |
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|
| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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"max_pending_pages": 1465,
|
| 173 |
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| 174 |
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| 175 |
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"upload_sustainable": null,
|
| 176 |
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|
| 177 |
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|
| 178 |
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"estimated_full_shards_per_hour": 0.0,
|
| 179 |
"selection_policy": "Smallest shard within 90% of measured local peak, enlarged to amortize 30 seconds of measured page production; bounded to 256 MiB."
|
| 180 |
},
|
| 181 |
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"upload_measurements": [],
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|
| 182 |
"cache_key": {
|
| 183 |
"version": 1,
|
| 184 |
"kind": "data",
|
|
|
|
| 221 |
},
|
| 222 |
"cache_reused": true,
|
| 223 |
"resume_probe_seconds": [
|
| 224 |
+
2.576500048000014,
|
| 225 |
+
0.012398547999964649
|
| 226 |
]
|
| 227 |
}
|
runs/void-byte-v1/events.out.tfevents.1791342976.f74d737f-395e-43a1-ae69-439b0359d002-2qp5c.418.0.1791342976705245012-6808dd50
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0f55a3d6927594e3f94c23b592fcd9076e21190d6cbbfb902c83fc265172973f
|
| 3 |
+
size 165
|